List of research papers focus on time series forecasting and deep learning, as well as other resources like competitions, datasets, courses, blogs, code, etc.
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- Nixtla’s
TimeGPT
is a generative pre-trained forecasting model for time series data.
- Nixtla’s
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13 Aug 2024, Lifan Zhao, et al.
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Bidirectional Generative Pre-training for Improving Time Series Representation Learning
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11 Aug 2024, Ziyang Song, et al.
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Scalable Transformer for High Dimensional Multivariate Time Series Forecasting
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08 Aug 2024, Xin Zhou, et al.
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RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms
- 06 Aug 2024, Luis Roque, et al.
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DAM: Towards A Foundation Model for Time Series Forecasting
- 25 Jul 2024, Luke Darlow, et al.
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A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting
- 22 Jul 2024, Pierre-Daniel Arsenault, et al.
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Deep Time Series Models: A Comprehensive Survey and Benchmark
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18 Jul 2024, Yuxuan Wang, et al.
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ViTime: A Visual Intelligence-Based Foundation Model for Time Series Forecasting
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10 Jul 2024, Luoxiao Yang, et al.
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S2IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting
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07 Jul 2024, Zijie Pan, et al.
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Fredformer: Frequency Debiased Transformer for Time Series Forecasting
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03 Jul 2024, Xihao Piao, et al.
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01 Jul 2024, Guoqi Yu, et al.
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29 Jun 2024, SheoYon Jhin, et al.
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SigKAN: Signature-Weighted Kolmogorov-Arnold Networks for Time Series
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25 Jun 2024, Hugo Inzirillo, et al.
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Are Language Models Actually Useful for Time Series Forecasting?
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22 Jun 2024, Mingtian Tan, et al.
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DeciMamba: Exploring the Length Extrapolation Potential of Mamba
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20 Jun 2024, Assaf Ben-Kish, et al.
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Understanding Different Design Choices in Training Large Time Series Models
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20 Jun 2024, Yu-Neng Chuang, et al.
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Foundation Models for Time Series Analysis: A Tutorial and Survey
- 18 Jun 2024, Yuxuan Liang, et al.
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Generative Pretrained Hierarchical Transformer for Time Series Forecasting
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18 Jun 2024, Zhiding Liu, et al.
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ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons
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17 Jun 2024, Jiawen Zhang, et al.
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LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction
- 16 Jun 2024, Meiyun Wang, et al.
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SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion
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12 Jun 2024, Lu Han, et al.
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A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
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11 Jun 2024, Yiyuan Yang, et al.
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[Official Code - Awesome-TimeSeries-SpatioTemporal-Diffusion-Model]
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Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift
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11 Jun 2024, Mouxiang Chen, et al.
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Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power and Interpretability
- 04 Jun 2024, Kunpeng Xu, et al.
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Timer: Generative Pre-trained Transformers Are Large Time Series Models
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04 Jun 2024, Yong Liu, et al.
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03 Jun 2024, Romain Ilbert, et al.
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SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters
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03 Jun 2024, Shengsheng Lin, et al.
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BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition
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30 May 2024, Shikai Fang, et al.
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Efficient and Effective Time-Series Forecasting with Spiking Neural Networks
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29 May 2024, Changze Lv, et al.
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ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks
- 28 May 2024, Wanlin Cai, et al.
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MambaTS: Improved Selective State Space Models for Long-term Time Series Forecasting
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26 May 2024, Xiuding Cai, et al.
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CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning
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23 May 2024, Peiyuan Liu, et al.
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TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting
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23 May 2024, Shiyu Wang, et al.
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GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing
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18 May 2024, Chengqing Yu, et al.
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Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting
- 17 May 2024, Aobo Liang, et al.
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DGCformer: Deep Graph Clustering Transformer for Multivariate Time Series Forecasting
- 14 May 2024, Qinshuo Liu, et al.
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Kolmogorov-Arnold Networks (KANs) for Time Series Analysis
- 14 May 2024, Cristian J. Vaca-Rubio, et al.
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TKAN: Temporal Kolmogorov-Arnold Networks
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12 May 2024, Remi Genet, et al.
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DTMamba : Dual Twin Mamba for Time Series Forecasting
- 11 May 2024, Zexue Wu, et al.
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Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting
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10 May 2024, Tianxiang Zhan, et al.
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07 May 2024, Jiexia Ye, et al.
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TSLANet: Rethinking Transformers for Time Series Representation Learning
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06 May 2024, Emadeldeen Eldele, et al.
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- 02 May 2024, Weijia Zhang, et al.
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Integrating Mamba and Transformer for Long-Short Range Time Series Forecasting
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23 Apr 2024, Xiongxiao Xu, et al.
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Biased Temporal Convolution Graph Network for Time Series Forecasting with Missing Values
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21 Apr 2024, Xiaodan Chen, et al.
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A decoder-only foundation model for time-series forecasting
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17 Apr 2024, Abhimanyu Das, et al.
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Towards Transparent Time Series Forecasting
- 15 Apr 2024, Krzysztof Kacprzyk, et al.
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- 09 Apr 2024, Vijay Ekambaram, et al.
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ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time Series Forecasting
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08 Apr 2024, Hengyu Ye, et al.
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04 Apr 2023, Xiao He, et al.
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Is Mamba Effective for Time Series Forecasting?
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02 Apr 2024, Zihan Wang, et al.
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TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting
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02 Apr 2024, Defu Cao, et al.
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From Similarity to Superiority: Channel Clustering for Time Series Forecasting
- 31 Mar 2024, Jialin Chen, et al.
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MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selection
- 29 Mar 2024, Ali Behrouz, et al.
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TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
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29 Mar 2024, Xiangfei Qiu, et al.
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An End-to-End Structure with Novel Position Mechanism and Improved EMD for Stock Forecasting
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25 Mar 2024, Chufeng Li, et al.
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HDMixer: Hierarchical Dependency with Extendable Patch for Multivariate Time Series Forecasting
- 24 Mar 2024, Qihe Huang, et al.
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Latent Diffusion Transformer for Probabilistic Time Series Forecasting
- 24 Mar 2024, Shibo Feng, et al.
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StockMixer: A Simple Yet Strong MLP-Based Architecture for Stock Price Forecasting
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24 Mar 2024, Jinyong Fan, et al.
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ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis
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22 Mar 2024, Donghao Luo, et al.
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SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series
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22 Mar 2024, Badri N. Patro, et al.
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An Analysis of Linear Time Series Forecasting Models
- 21 Mar 2024, William Toner, et al.
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Self-Supervised Learning for Time Series: Contrastive or Generative?
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14 Mar 2024, Ziyu Liu, et al.
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TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting
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14 Mar 2024, Md Atik Ahamed, et al.
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TimeDRL: Disentangled Representation Learning for Multivariate Time-Series
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13 Mar 2024, Ching Chang, et al.
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Chronos: Learning the Language of Time Series
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12 Mar 2024, Abdul Fatir Ansari, et al.
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Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning
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10 Mar 2024, Yuxuan Bian, et al.
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MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process
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09 Mar 2024, Xinyao Fan, et al.
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08 Mar 2024, Muyao Wang, et al.
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Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting
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07 Mar 2024, Peng Chen, et al.
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Periodicity Decoupling Framework for Long-term Series Forecasting
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06 Mar 2024, Tao Dai, et al.
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- 05 Mar 2024, Ce Chi, et al.
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04 Mar 2024, Jiecheng Lu, et al.
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Diffusion-TS: Interpretable Diffusion for General Time Series Generation
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04 Mar 2024, Xinyu Yuan, et al.
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Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models
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29 Feb 2024, Kelvin Koa, et al.
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TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables
- 29 Feb 2024, Yuxuan Wang, et al.
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UniTS: Building a Unified Time Series Model
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29 Feb 2024, Shanghua Gao, et al.
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TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis
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26 Feb 2024, Sabera Talukder, et al.
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LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting
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25 Feb 2024, Haoxin Liu, et al.
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TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series
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22 Feb 2024, Chenxi Sun, et al.
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CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting
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16 Feb 2024, Wang Xue, et al.
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ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling
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16 Feb 2024, Yuqi Chen, et al.
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Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review
- 15 Feb 2024, Jing Su, et al.
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Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
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08 Feb 2024, Kashif Rasul, et al.
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- 08 Feb 2024, Linfeng Du, et al.
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MOMENT: A Family of Open Time-series Foundation Models
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06 Feb 2024, Mononito Goswami, et al.
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DiffsFormer: A Diffusion Transformer on Stock Factor Augmentation
- 05 Feb 2024, Yuan Gao, et al.
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Position Paper: What Can Large Language Models Tell Us about Time Series Analysis
- 05 Feb 2024, Ming Jin, et al.
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AutoTimes: Autoregressive Time Series Forecasters via Large Language Models
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04 Feb 2024, Yong Liu, et al.
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FreDF: Learning to Forecast in Frequency Domain
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04 Feb 2024, Hao Wang, et al.
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Unified Training of Universal Time Series Forecasting Transformers
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04 Feb 2024, Gerald Woo, et al.
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Large Language Models for Time Series: A Survey
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02 Feb 2024, Xiyuan Zhang, et al.
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A Survey of Deep Learning and Foundation Models for Time Series Forecasting
- 25 Jan 2024, John A. Miller, et al.
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LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters
- 18 Jan 2024, Ching Chang, et al.
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MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series Forecasting
- 17 Jan 2024, Zongjiang Shang, et al.
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RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Tasks
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17 Jan 2024, Haowen Hou, et al.
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CNN Kernels Can Be the Best Shapelets
- 16 Jan 2024, Eric Qu, et al.
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- 16 Jan 2024, Xiaoyi Liu, et al.
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Generative Learning for Financial Time Series with Irregular and Scale-Invariant Patterns
- 16 Jan 2024, Hongbin Huang, et al.
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Leveraging Generative Models for Unsupervised Alignment of Neural Time Series Data
- 16 Jan 2024, Ayesha Vermani, et al.
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Self-Supervised Contrastive Learning for Long-term Forecasting
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16 Jan 2024, Junwoo Park, et al.
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SocioDojo: Building Lifelong Analytical Agents with Real-world Text and Time Series
- 16 Jan 2024, Junyan Cheng, et al.
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HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling for Long-Term Forecasting
- 10 Jan 2024, Shubao Zhao, et al.
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Universal Time-Series Representation Learning: A Survey
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08 Jan 2024, Patara Trirat, et al.
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UnetTSF: A Better Performance Linear Complexity Time Series Prediction Model
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05 Jan 2024, Chu Li, et al.
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U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting
- 04 Jan 2024, Ling Chen, et al.
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MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting
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31 Dec 2023, Wanlin Cai, et al.
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28 Dec 2023, Zhihao Yu, et al.
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TSPP: A Unified Benchmarking Tool for Time-series Forecasting
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28 Dec 2023, Jan Bączek, et al.
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Continuous-time Autoencoders for Regular and Irregular Time Series Imputation
- 27 Dec 2023, Hyowon Wi, et al.
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Learning to Embed Time Series Patches Independently
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27 Dec 2023, Seunghan Lee, et al.
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TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learning
- 25 Dec 2023, Jiexi Liu, et al.
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AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting
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20 Dec 2023, Raphael Fischer, et al.
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CGS-Mask: Making Time Series Predictions Intuitive for All
- 15 Dec 2023, Feng Lu, et al.
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Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting
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14 Dec 2023, Yanhong Li, et al.
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SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation
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10 Dec 2023, Hyun Ryu, et al.
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Mamba: Linear-Time Sequence Modeling with Selective State Spaces
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01 Dec 2023, Albert Gu, et al.
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Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting
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24 Nov 2023, Yong Liu, et al.
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FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective
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10 Nov 2023, Kun Yi, et al.
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Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
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10 Nov 2023, Kun Yi, et al.
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Multi-resolution Time-Series Transformer for Long-term Forecasting
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07 Nov 2023, Yitian Zhang, et al.
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- 07 Nov 2023, Hao Liu, et al.
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BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis
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31 Oct 2023, Zelin Ni, et al.
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ProNet: Progressive Neural Network for Multi-Horizon Time Series Forecasting
- 30 Oct 2023, Yang Lin
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Hierarchical Ensemble-Based Feature Selection for Time Series Forecasting
- 26 Oct 2023, Ayşın Tümay, et al.
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Attention-Based Ensemble Pooling for Time Series Forecasting
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24 Oct 2023, Dhruvit Patel, et al.
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19 Oct 2023, Ioannis Nasios, et al.
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A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis
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18 Oct 2023, Shuhan Zhong, et al.
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Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
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16 Oct 2023, Ming Jin, et al.
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UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting
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15 Oct 2023, Xu Liu, et al.
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Lag-Llama: Towards Foundation Models for Time Series Forecasting
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12 Oct 2023, Kashif Rasul, et al.
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Large Language Models Are Zero-Shot Time Series Forecasters
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11 Oct 2023, Nate Gruver, et al.
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iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
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10 Oct 2023, Yong Liu, et al.
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Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain
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08 Oct 2023, Gerald Woo, et al.
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Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs
- 04 Oct 2023, Ilan Naiman, et al.
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Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
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03 Oct 2023, Ming Jin, et al.
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Modality-aware Transformer for Time series Forecasting
- 02 Oct 2023, Hajar Emami, et al.
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PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecasting
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01 Oct 2023, Zeying Gong, et al.
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Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective
- 22 Sep 2023, Zhiding Liu, et al.
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OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
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22 Sep 2023, Yi-Fan Zhang, et al.
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WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting
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20 Sep 2023, Peiyuan Liu, et al.
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Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data
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11 Sep 2023, Yucheng Wang, et al.
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PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series
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25 Aug 2023, Nicasia Beebe-Wang, et al.
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TFDNet: Time-Frequency Enhanced Decomposed Network for Long-term Time Series Forecasting
- 25 Aug 2023, Yuxiao Luo, et al.
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- 24 Aug 2023, Marcial Sanchis-Agudo, et al.
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Multi-scale Transformer Pyramid Networks for Multivariate Time Series Forecasting
- 23 Aug 2023, Yifan Zhang, et al.
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SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting
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22 Aug 2023, Shengsheng Lin, et al.
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LLM4TS: Two-Stage Fine-Tuning for Time-Series Forecasting with Pre-Trained LLMs
- 16 Aug 2023, Ching Chang, et al.
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PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformer
- 09 Aug 2023, Shengsheng Lin, et al.
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DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Prediction
- 07 Aug 2023, Chengqing Yu, et al.
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Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting
- 04 Aug 2023, Arindam Jati, et al.
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Unsupervised Representation Learning for Time Series: A Review
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03 Aug 2023, Qianwen Meng, et al.
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Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion
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02 Aug 2023, Aurélien Renault, et al.
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02 Aug 2023, Chunwei Yang, et al.
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SimpleTS: An Efficient and Universal Model Selection Framework for Time Series Forecasting
- 01 Aug 2023, Yuanyuan Yao, et al.
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DeepTSF: Codeless machine learning operations for time series forecasting
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28 Jul 2023, Sotiris Pelekis, et al.
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TimeGNN: Temporal Dynamic Graph Learning for Time Series Forecasting
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27 Jul 2023, Nancy Xu, et al.
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TransFusion: Generating Long, High Fidelity Time Series using Diffusion Models with Transformers
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24 Jul 2023, Md Fahim Sikder, et al.
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Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting
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21 Jul 2023, Marcel Kollovieh, et al.
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19 Jul 2023, Jianing Hao, et al.
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Look Ahead: Improving the Accuracy of Time-Series Forecasting by Previewing Future Time Features
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18 July 2023, Seonmin Kim, et al.
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GBT: Two-stage transformer framework for non-stationary time series forecasting
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17 Jul 2023, Li Shen, et al.
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Sequential Monte Carlo Learning for Time Series Structure Discovery
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13 Jul 2023, Feras A. Saad, et al.
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07 Jul 2023, Ming Jin, et al.
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GEANN: Scalable Graph Augmentations for Multi-Horizon Time Series Forecasting
- 07 Jul 2023, Sitan Yang, et al.
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FITS: Modeling Time Series with 10k Parameters
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06 Jul 2023, Zhijian Xu, et al.
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SAITS: Self-Attention-based Imputation for Time Series
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05 Jul 2023, Wenjie Du, et al.
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SageFormer: Series-Aware Graph-Enhanced Transformers for Multivariate Time Series Forecasting
- 04 Jul 2023, Zhenwei Zhang, et al.
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ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection
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03 Jul 2023, Yuhang Chen, et al.
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Precursor-of-Anomaly Detection for Irregular Time Series
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27 Jun 2023, SheoYon Jhin, et al.
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Anomaly Detection with Score Distribution Discrimination
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26 Jun 2023, Minqi Jiang, et al.
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- 26 Jun 2023, Haizhou Cao, et al.
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Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting
- 19 Jun 2023, Xinli Yu, et al.
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DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
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17 Jun 2023, Yiyuan Yang, et al.
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16 Jun 2023, Iman Deznabi, et al.
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Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
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16 Jun 2023, Kexin Zhang, et al.
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14 Jun 2023, YanJun Zhao, et al.
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TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting
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14 Jun 2023, Vijay Ekambaram, et al.
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Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping
- 12 Jun 2023, Luxuan Wang, et al.
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Feature Programming for Multivariate Time Series Prediction
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09 Jun 2023, Alex Reneau, et al.
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Self-Interpretable Time Series Prediction with Counterfactual Explanations
- 09 Jun 2023, Jingquan Yan, et al.
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Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations
- 09 Jun 2023, Etienne Le Naour, et al.
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Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency
- 03 Jun 2023, Owen Queen, et al.
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An End-to-End Time Series Model for Simultaneous Imputation and Forecast
- 01 Jun 2023, Trang H. Tran, et al.
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Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context
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01 Jun 2023, Oussama Boussif, et al.
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30 May 2023, Jiaxin Gao, et al.
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Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
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30 May 2023, Yong Liu, et al.
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Learning Perturbations to Explain Time Series Predictions
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30 May 2023, Joseph Enguehard.
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TLNets: Transformation Learning Networks for long-range time-series prediction
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25 May 2023, Wei Wang, et al.
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A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecasting
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24 May 2023, Yushu Chen, et al.
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Forecasting Irregularly Sampled Time Series using Graphs
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22 May 2023, Vijaya Krishna Yalavarthi, et al.
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22 May 2023, Jinliang Deng, et al.
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Make Transformer Great Again for Time Series Forecasting: Channel Aligned Robust Dual Transformer
- 20 May 2023, Wang Xue, et al.
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Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping
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18 May 2023, Zhe Li, et al.
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How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?
- 11 May 2023, Ming Jin, et al.
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IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solvers
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11 May 2023, Jingge Xiao, et al.
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CUTS+: High-dimensional Causal Discovery from Irregular Time-series
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10 May 2023, Yuxiao Cheng, et al.
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Causal Discovery from Subsampled Time Series with Proxy Variables
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09 May 2023, Mingzhou Liu, et al.
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Mlinear: Rethink the Linear Model for Time-series Forecasting
- 08 May 2023, Wei Li, et al.
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Diffusion Models for Time Series Applications: A Survey
- 01 May 2023, Lequan Lin, et al.
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Context Consistency Regularization for Label Sparsity in Time Series
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25 Apr 2023, Yooju Shin, et al.
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Prototype-oriented unsupervised anomaly detection for multivariate time series
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25 Apr 2023, Yuxin Li, et al.
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Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series
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25 Apr 2023, Aniruddh Raghu, et al.
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- 21 Apr 2023, Cheng Zhang, et al.
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Long-term Forecasting with TiDE: Time-series Dense Encoder
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17 Apr 2023, Abhimanyu Das, et al.
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[Official Code - google-research - tide] [Unofficial Implementation - TiDE]
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Financial Time Series Forecasting using CNN and Transformer
- 11 Apr 2023, Zhen Zeng, et al.
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11 Apr 2023, Lu Han, et al.
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Handling Concept Drift in Global Time Series Forecasting
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04 Apr 2023, Ziyi Liu, et al.
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SimTS: Rethinking Contrastive Representation Learning for Time Series Forecasting
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31 Mar 2023, Xiaochen Zheng, et al.
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Towards Diverse and Coherent Augmentation for Time-Series Forecasting
- 24 Mar 2023, Xiyuan Zhang, et al.
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UniTS: A Universal Time Series Analysis Framework with Self-supervised Representation Learning
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24 Mar 2023, Zhiyu Liang, et al.
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Conformal Prediction for Time Series with Modern Hopfield Networks
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22 Mar 2023, Andreas Auer, et al.
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- 21 Mar 2023, Dapeng Li, et al.
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Late Meta-learning Fusion Using Representation Learning for Time Series Forecasting
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20 Mar 2023, Terence L van Zyl.
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Discovering Predictable Latent Factors for Time Series Forecasting
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18 Mar 2023, Jingyi Hou, et al.
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TSMixer: An All-MLP Architecture for Time Series Forecasting
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10 Mar 2023, Si-An Chen, et al.
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PHILNet: A novel efficient approach for time series forecasting using deep learning
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08 Mar 2023, M.J. Jiménez-Navarro, et al.
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Time Series Forecasting with Transformer Models and Application to Asset Management
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28 Feb 2023, Luoxiao Yang, et al.
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[Official Code - machine-vision-assisted-deep-time-series-analysis-MV-DTSA-]
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LightCTS: A Lightweight Framework for Correlated Time Series Forecasting
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23 Feb 2023, Zhichen Lai, et al.
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One Fits All:Power General Time Series Analysis by Pretrained LM
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23 Feb 2023, Tian Zhou, et al.
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Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting
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22 Feb 2023, Wei Fan, et al.
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FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification
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20 Feb 2023, Mingyue Cheng, et al.
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FrAug: Frequency Domain Augmentation for Time Series Forecasting
- 18 Feb 2023, Muxi Chen, et al.
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Improved Online Conformal Prediction via Strongly Adaptive Online Learning
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15 Feb 2023, Aadyot Bhatnagar, et al.
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SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies
- 11 Feb 2023, Fan Zhou, et al.
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MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing
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09 Feb 2023, Zhe Li, et al.
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Domain Adaptation for Time Series Under Feature and Label Shifts
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06 Feb 2023, Huan He, et al.
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02 Feb 2023, Yunhao Zhang, Junchi Yan
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MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting
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02 Feb 2023, Huiqiang Wang, et al.
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SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling
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02 Feb 2023, Jiaxiang Dong, et al.
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PrimeNet : Pre-Training for Irregular Multivariate Time Series
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AAAI 2023, Ranak Roy Chowdhury, et al.
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- 27 Jan 2023, Hui He, et al.
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Improving Text-based Early Prediction by Distillation from Privileged Time-Series Text
- 26 Jan 2023, Jinghui Liu, et al.
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Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series
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26 Jan 2023, Abdul Fatir Ansari, et al.
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Multi-view Kernel PCA for Time series Forecasting
- 24 Jan 2023, Arun Pandey, et al.
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Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement
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08 Jan 2023, Yan Li, et al.
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Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution
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05 Jan 2023, Yan Li, et al.
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Infomaxformer: Maximum Entropy Transformer for Long Time-Series Forecasting Problem
- 04 Jan 2023, Peiwang Tang, et al.
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Neural SDEs for Conditional Time Series Generation and the Signature-Wasserstein-1 metric
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03 Jan 2023, Pere Díaz Lozano, et al.
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28 Dec 2022, Shiyu Wang, et al.
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Dynamic Sparse Network for Time Series Classification: Learning What to "see"
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19 Dec 2022, Qiao Xiao, et al.
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Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting
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18 Dec 2022, Slawek Smyl, et al.
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Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation
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16 Dec 2022, Yizhou Dang, et al.
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First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting
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15 Dec 2022, Xiyuan Zhang, et al.
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[Code]
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Put Attention to Temporal Saliency Patterns of Multi-Horizon Time Series
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15 Dec 2022, Nghia Duong-Trung, et al.
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Area2Area Forecasting: Looser Constraints, Better Predictions (Manuscript submitted to journal Information Sciences)
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Sequential Predictive Conformal Inference for Time Series
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07 Dec 2022, Chen Xu, et al.
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06 Dec 2022, Zanwei Zhou, et al.
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DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting
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06 Dec 2022, Shiyong Lan, et al.
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Learning of Cluster-based Feature Importance for Electronic Health Record Time-series
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06 Dec 2022, Henrique Aguiar, et al.
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CoTMix: Contrastive Domain Adaptation for Time-Series via Temporal Mixup
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03 Dec 2022, Emadeldeen Eldele, et al.
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FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting
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02 Dec 2022, Maowei Jiang, et al.
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MHCCL: Masked Hierarchical Cluster-wise Contrastive Learning for Multivariate Time Series
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02 Dec 2022, Qianwen Meng, et al.
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CRU: A Novel Neural Architecture for Improving the Predictive Performance of Time-Series Data
- 30 Nov 2022, Sunghyun Sim, et al.
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AirFormer: Predicting Nationwide Air Quality in China with Transformers
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29 Nov 2022, Yuxuan Liang, et al.
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Learning Latent Seasonal-Trend Representations for Time Series Forecasting
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29 Nov 2022, Zhiyuan Wang, et al.
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A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
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27 Nov 2022, Yuqi Nie, et al.
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A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting
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05 Nov 2022, Aryan Jadon, et al.
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Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion
- 04 Nov 2022, Marin Biloš, et al.
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Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks
- 01 Nov 2022, Yijing Liu, et al.
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- 01 Nov 2022, Yuzhou Chen, et al.
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TILDE-Q: A Transformation Invariant Loss Function for Time-Series Forecasting
- 26 Oct 2022, Hyunwook Lee, et al.
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WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting
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25 Oct 2022, Youngin Cho, et al.
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SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction
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13 Oct 2022, Minhao Liu, et al
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Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts
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07 Oct 2022, Rui Wang, et al.
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TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
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05 Oct 2022, Haixu Wu, et al.
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Retrieval Based Time Series Forecasting
- 27 Sep 2022, Baoyu Jing, et al.
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FDNet: Focal Decomposed Network for Efficient, Robust and Practical Time Series Forecasting
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22 Sep 2022, Li Shen, et al.
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PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting
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20 Sep 2022, Hao Xue, et al.
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Out-of-Distribution Representation Learning for Time Series Classification
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15 Sep 2022, Wang Lu, et al.
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Statistical, machine learning and deep learning forecasting methods: Comparisons and ways forward
- 05 Sep 2022, Spyros Makridakis, et al.
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Expressing Multivariate Time Series as Graphs with Time Series Attention Transformer
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19 Aug 2022, William T. Ng, et al.
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Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting
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14 Aug 2022, Zezhi Shao, et al.
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Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting
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10 Aug 2022, Zezhi Shao, et al.
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Respecting Time Series Properties Makes Deep Time Series Forecasting Perfect
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22 Jul 2022, Li Shen, et al.
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Formal Algorithms for Transformers
- 19 Jul 2022, Mary Phuong, Marcus Hutter
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Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms
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19 Jul 2022, Linbo Liu, et al.
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Generalizable Memory-driven Transformer for Multivariate Long Sequence Time-series Forecasting
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16 Jul 2022, Xiaoyun Zhao, et al.
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Learning Deep Time-index Models for Time Series Forecasting
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13 Jul 2022, Gerald Woo, et al.
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Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes
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13 Jul 2022, Gregory Benton, et al.
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Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures
- 04 Jul 2022, Tianping Zhang, et al.
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CATN: Cross Attentive Tree-Aware Network for Multivariate Time Series Forecasting
- 28 Jun 2022, Hui He, et al.
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Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting
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28 Jun 2022, Junchen Ye, et al
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Utilizing Expert Features for Contrastive Learning of Time-Series Representations
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23 Jun 2022, Manuel Nonnenmacher, et al.
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Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency
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17 Jun 2022, Xiang Zhang, et al.
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Closed-Form Diffeomorphic Transformations for Time Series Alignment
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16 Jun 2022, Iñigo Martinez, et al.
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Contrastive Learning for Unsupervised Domain Adaptation of Time Series
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13 Jun 2022, Yilmazcan Ozyurt, et al.
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Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting
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08 Jun 2022, Amin Shabani, et al.
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31 May 2022, Iris A.M. Huijben, et al.
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Are Transformers Effective for Time Series Forecasting?
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26 May 2022, Ailing Zeng, et al.
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FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
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18 May 2022, Tian Zhou, et al.
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Efficient Automated Deep Learning for Time Series Forecasting
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11 May 2022, Difan Deng, et al.
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Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Version [An introduction]
- 28 Apr 2022, Razvan-Gabriel Cirstea, et al.
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25 Apr 2022, Sheo Yon Jhin, et al.
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Multi-Granularity Residual Learning with Confidence Estimation for Time Series Prediction
- 25 Apr 2022, Min Hou, et al.
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RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph
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25 Apr 2022, Ruijie Wang, et al.
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A data filling methodology for time series based on CNN and (Bi)LSTM neural networks
- 21 Apr 2022, Kostas Tzoumpas, et al.
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ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data
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15 Mar 2022, Mohamed Ragab, et al.
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DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting
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15 Mar 2022, Wei Fan, et al.
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23 Feb 2022, Dazhao Du, et al.
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[Code]
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Adaptive Conformal Predictions for Time Series
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15 Feb 2022, Margaux Zaffran, et al.
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ST-GSP: Spatial-Temporal Global Semantic Representation Learning for Urban Flow Prediction
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15 Feb 2022, Liang Zhao, et al.
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Transformers in Time Series: A Survey
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15 Feb 2022, Qingsong Wen, et al.
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TACTiS: Transformer-Attentional Copulas for Time Series
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7 Feb 2022, Alexandre Drouin, et al.
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03 Feb 2022, Gerald Woo, et al.
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ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
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03 Feb 2022, Gerald Woo, et al.
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FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
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30 Jan 2022, Tian Zhou, et al.
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N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting
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30 Jan 2022, Cristian Challu, et al.
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Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift
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29 Jan 2022, Taesung Kim, et al.
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Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting
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13 Jan 2022, Ling Chen, et al.
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AutoCTS: Automated Correlated Time Series Forecasting -- Extended Version
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21 Dec 2021, Xinle Wu, et al.
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A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models
- 17 Dec 2021, Saroj Gopali, et al.
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TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs
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15 Dec 2021, Yushan Liu, et al.
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Parameter Efficient Deep Probabilistic Forecasting
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14 Dec 2021, Olivier Sprangers, et al.
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NeuralProphet: Explainable Forecasting at Scale
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29 Nov 2021, Oskar Triebe, et al.
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Modeling Irregular Time Series with Continuous Recurrent Units
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22 Nov 2021, Mona Schirmer, et al.
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Transferable Time-Series Forecasting under Causal Conditional Shift
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05 Nov 2021, Zijian Li, et al.
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Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series
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04 Nov 2021, Daniel Kramer, et al.
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ClaSP - Time Series Segmentation
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30 Oct 2021, Patrick Schäfer, et al.
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26 Oct 2021, Wentao Xu, et al.
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Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting
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13 Oct 2021, Kiran Madhusudhanan, et al.
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Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
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06 Oct 2021, Jiehui Xu, et al.
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CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learning
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30 Sep 2021, Garrett Wilson, et al.
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Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting
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29 Sep 2021, Shizhan Liu, et al.
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[Code]
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Long-Range Transformers for Dynamic Spatiotemporal Forecasting
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24 Sep 2021, Jake Grigsby, et al
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DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications
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23 Sep 2021, Dongqi Han, et al.
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CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting
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15 Sep 2021, Harshavardhan Kamarthi, et al.
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Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation
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10 Sep 2021, Ziluo Ding, et al.
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TCCT: Tightly-Coupled Convolutional Transformer on Time Series Forecasting
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29 Aug 2021, Li Shen, Yangzhu Wang
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Machine learning in the Chinese stock market
- 27 Aug 2021, Markus Leippold, et al.
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Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization
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14 Aug 2021, Ahmed Abdulaal, et al.
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AdaRNN: Adaptive Learning and Forecasting of Time Series
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10 Aug 2021, Yuntao Du, et al.
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CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation
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07 Jul 2021, Yusuke Tashiro, et al.
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Spatiotemporal information conversion machine for time-series prediction
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03 Jul 2021, Hao Peng, et al.
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Time-Series Representation Learning via Temporal and Contextual Contrasting
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26 Jun 2021, Emadeldeen Eldele, et al.
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Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
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24 Jun 2021, Haixu Wu, et al.
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[Code]
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TS2Vec: Towards Universal Representation of Time Series
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19 Jun 2021, Zhihan Yue, et al.
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[Code]
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18 Jun 2021, Tijin Yan, et al.
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Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction
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17 Jun 2021, Minhao Liu, et al.
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[Code]
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Voice2Series: Reprogramming Acoustic Models for Time Series Classification
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17 Jun 2021, Chao-Han Huck Yang, et al.
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Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding
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01 Jun 2021, Sana Tonekaboni, et al.
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Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting
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10 May 2021, Yuzhou Chen, et al.
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12 Apr 2021, Kin G. Olivares, et al.
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[Code]
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An Experimental Review on Deep Learning Architectures for Time Series Forecasting
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22 Mar 2021, Pedro Lara-Benítez, et al.
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Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
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13 Mar 2021, Defu Cao, et al.
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FluxEV: A Fast and Effective Unsupervised Framework for Time-Series Anomaly Detection
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08 Mar 2021, Jia Li, et al.
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Perceiver: General Perception with Iterative Attention
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04 Mar 2021, Andrew Jaegle, et al.
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03 Mar 2021, Yinjun Wu, et al.
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Domain Adaptation for Time Series Forecasting via Attention Sharing
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13 Feb 2021, Xiaoyong Jin, et al.
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Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting
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31 Jan 2021, Longyuan Li, et al.
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Adjusting for Autocorrelated Errors in Neural Networks for Time Series
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28 Jan 2021, Fan-Keng Sun, et al.
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Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
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28 Jan 2021, Kashif Rasul, et al.
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Long Horizon Forecasting With Temporal Point Processes
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08 Jan 2021, Prathamesh Deshpande, et al.
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Do We Really Need Deep Learning Models for Time Series Forecasting?
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06 Jan 2021, Shereen Elsayed, et al.
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[Code]
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Conditional Local Convolution for Spatio-temporal Meteorological Forecasting
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04 Jan 2021, Haitao Lin, et al.
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Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
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14 Dec 2020, Haoyi Zhou, et al.
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[Code]
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TimeSHAP: Explaining Recurrent Models through Sequence Perturbations
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30 Nov 2020, João Bento, et al.
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Conformal prediction for time series
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18 Oct 2020, Chen Xu, et al.
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A Transformer-based Framework for Multivariate Time Series Representation Learning
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06 Oct 2020, George Zerveas, et al.
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[Code]
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Deep Switching Auto-Regressive Factorization:Application to Time Series Forecasting
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10 Sep 2020, Amirreza Farnoosh, et al.
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Deep Learning for Anomaly Detection: A Review
- 06 Jul 2020, Guansong Pang, et al.
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On Multivariate Singular Spectrum Analysis and its Variants
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24 Jun 2020, Anish Agarwal, et al.
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Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
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24 May 2020, Zonghan Wu, et al.
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Time Series Data Augmentation for Deep Learning: A Survey
- 27 Feb 2020, Qingsong Wen, et al.
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Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case
- 23 Jan 2020, Neo Wu, et al.
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Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
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19 Dec 2019, Bryan Lim, et al.
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[Code]
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Towards Better Forecasting by Fusing Near and Distant Future Visions
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11 Dec 2019, Jiezhu Cheng, et al.
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Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019
- 29 Nov 2019, Omer Berat Sezer, et al.
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DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting
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03 Nov 2019, Siteng Huang, et al.
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03 Nov 2019, Won-Seok Hwang, et al.
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High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes
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07 Oct 2019, David Salinas, et al.
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[Code]
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Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
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19 Sep 2019, Vincent Le Guen, et al.
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InceptionTime: Finding AlexNet for Time Series Classification
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11 Sep 2019, Hassan Ismail Fawaz, et al.
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Time2Vec: Learning a Vector Representation of Time
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11 Jul 2019, Seyed Mehran Kazemi, et al.
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[Code]
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Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting
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29 Jun 2019, Shiyang Li, et al.
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[Code] [Community Code]
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Probabilistic Forecasting with Temporal Convolutional Neural Network
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11 Jun 2019, Yitian Chen, et al.
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N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
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24 May 2019, Boris N. Oreshkin, et al.
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[Code]
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Time-Series Event Prediction with Evolutionary State Graph
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10 May 2019, Wenjie Hu, et al.
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Deep Adaptive Input Normalization for Time Series Forecasting
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21 Feb 2019, Nikolaos Passalis, et al.
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Unsupervised Scalable Representation Learning for Multivariate Time Series
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30 Jan 2019, Jean-Yves Franceschi, et al.
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Causal Discovery with Attention-Based Convolutional Neural Networks
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07 Jan 2019, Meike Nauta, et al.
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RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
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05 Dec 2018, Qingsong Wen, et al.
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[Code]
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Deep learning for time series classification: a review
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12 Sep 2018, Hassan Ismail Fawaz, et al.
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BRITS: Bidirectional Recurrent Imputation for Time Series
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27 May 2018, Wei Cao, et al.
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An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
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19 Apr 2018, Shaojie Bai, et al.
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Universal features of price formation in financial markets: perspectives from Deep Learning
- 19 Mar 2018, Justin Sirignano, et al.
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30 Oct 2017, Petar Veličković, et al.
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[Code]
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12 Jun 2017, Ashish Vaswani, et al.
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[Code]
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Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
- 12 Jun 2017, Xingjian Shi, et al.
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DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
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13 Apr 2017, David Salinas, et al.
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[Code]
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Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
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21 Mar 2017, Guokun Lai, et al.
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Kolmogorov-Arnold Networks (KANs) for Time Series Forecasting
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Time-Series Forecasting: Deep Learning vs Statistics — Who Wins?
-
aeon
is an open-source toolkit for learning from time series.
-
- Autoregressive Conditional Heteroskedasticity (ARCH) and other tools for financial econometrics, written in Python (with Cython and/or Numba used to improve performance)
-
- A Julia package for learning the covariance structure of Gaussian process time series models.
-
AutoTS
is a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale.
-
BasicTS
(Basic Time Series) is a PyTorch-based benchmark and toolbox for time series forecasting (TSF).
-
Beibo
is a Python library that uses several AI prediction models to predict stocks returns over a defined period of time.
-
Cesium
is an end-to-end machine learning platform for time-series, from calculation of features to model-building to predictions.
-
Darts
is a Python library for easy manipulation and forecasting of time series.
-
DeepOD
is an open-source python framework for deep learning-based anomaly detection on multivariate data.
-
Flow Forecast
is a deep learning PyTorch library for time series forecasting, classification, and anomaly detection.
-
functime
is a powerful Python library for production-ready global forecasting and time-series feature extraction on large panel datasets.
-
GluonTS
is a Python package for probabilistic time series modeling, focusing on deep learning based models.
-
- The
Greykite
library provides flexible, intuitive and fast forecasts through its flagship algorithm, Silverkite.
- The
-
- A Full-Pipeline Automated Time Series (AutoTS) Analysis Toolkit.
-
Kats
is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis.
-
Luminaire
is a python package that provides ML-driven solutions for monitoring time series data.
-
- A scikit-learn-compatible module for estimating prediction intervals.
-
Merlion
is a Python library for time series intelligence. It provides an end-to-end machine learning framework that includes loading and transforming data, building and training models, post-processing model outputs, and evaluating model performance.
-
NeuralForecast
is a Python library for time series forecasting with deep learning models.
-
NeuralProphet
is an easy to learn framework for interpretable time series forecasting. NeuralProphet is built on PyTorch and combines Neural Network and traditional time-series algorithms, inspired by Facebook Prophet and AR-Net.
-
- PaddlePaddle-based Time Series Modeling in Python.
-
- Pandas Technical Analysis (
Pandas TA
) is an easy to use library that leverages the Pandas package with more than 130 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns.
- Pandas Technical Analysis (
-
Prophet
is a forecasting procedure implemented in R and Python. It is fast and provides completely automated forecasts that can be tuned by hand by data scientists and analysts.
-
Puncc
is a python library for predictive uncertainty quantification using conformal prediction.
-
PyBATS
is a package for Bayesian time series modeling and forecasting.
-
- A Python package to discover stochastic differential equations from time series data.
-
PyDMD: Python Dynamic Mode Decomposition
PyDMD
is a Python package that uses Dynamic Mode Decomposition for a data-driven model simplification based on spatiotemporal coherent structures.
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- An open source library for Fuzzy Time Series in Python.
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- A Python Toolbox for Data Mining on Partially-Observed Time Series.
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Python Outlier Detection (PyOD)
PyOD
is a comprehensive and scalable Python library for outlier detection (anomaly detection)
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PyTorch Forecasting
is a PyTorch-based package for forecasting time series with state-of-the-art network architectures.
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pytrendseries
is a Python library for detection of trends in time series like: stock prices, monthly sales, daily temperature of a city and so on.
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pyts
is a Python package dedicated to time series classification.
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Qlib
is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment.
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- A extendable, replaceable Python algorithmic backtest & trading framework supporting multiple securities.
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- The pratictioner's forecasting library. Including automated model selection, model optimization, pipelines, visualization, and reporting.
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sequitur
is a library that lets you create and train an autoencoder for sequential data in just two lines of code.
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Skforecast
is a Python library that eases using scikit-learn regressors as single and multi-step forecasters. It also works with any regressor compatible with the scikit-learn API (LightGBM, XGBoost, CatBoost, ...).
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sktime
is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks.
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StatsForecast
offers a collection of popular univariate time series forecasting models optimized for high performance and scalability.
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TFTS
(TensorFlow Time Series) is an easy-to-use python package for time series, supporting the classical and SOTA deep learning methods in TensorFlow or Keras.
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tft-torch
is a Python library that implements "Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting" using pytorch framework.
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TimeEval
is an evaluation tool for time series anomaly detection algorithms.
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- This library expands the Captum library with a specific focus on time-series.
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TSlib
is an open-source library for deep learning researchers, especially deep time series analysis.
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TODS
is a full-stack automated machine learning system for outlier detection on multivariate time-series data.
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- Machine learning for transportation data imputation and prediction.
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tsai
is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series tasks like classification, regression, forecasting, imputation...
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tsam
is a python package which uses different machine learning algorithms for the aggregation of time series.
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tsaug
is a Python package for time series augmentation.
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- A Python Toolbox to Ease Loading Open-Source Time-Series Datasets.
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- Calculates various features from time series data. Python implementation of the R package tsfeatures.
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- Time Series Feature Extraction Library (TSFEL for short) is a Python package for feature extraction on time series data.
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tsfresh
provides systematic time-series feature extraction by combining established algorithms from statistics, time-series analysis, signal processing, and nonlinear dynamics with a robust feature selection algorithm.
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tslearn
is a Python package that provides machine learning tools for the analysis of time series.
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- A python package for time series forecasting with scikit-learn estimators.
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Forecasting: Principles and Practice (3rd ed)
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Rob J Hyndman and George Athanasopoulos, 2021
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This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly.
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- List of state of the art papers, code, and other resources focus on time series forecasting.
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- This curated list contains python packages for time series analysis.
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- This is the repository for the collection of deep learning in stock market prediction.
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- Repository of Transformer based PyTorch Time Series Models.
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- Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series (Powered by PyTorch).
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Deep Learning and Machine Learning for Stock Predictions
- This is for learning, studying, researching, and analyzing stock in deep learning (DL) and machine learning (ML).
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Feature Engineering for Time Series Forecasting
- Create lag, window and seasonal features, perform imputation, variable encoding, extract features from datetime, remove outliers, and more.
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- Gathers machine learning and deep learning models for Stock forecasting, included trading bots and simulations.
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time-series-forecasting-with-python
- A use-case focused tutorial for time series forecasting with python.