Jan 10, 2024 · As others mentioned, no unique model or paradigm is better than the rest in all cases. Deep-learning models are the SoTA in many industry applications and are used in production by large companies for demand forecasting (e.g., Amazon, Zalando, Walmart, and so on).
Apr 10, 2023 · In recent years, Deep Learning has made remarkable progress in the field of NLP. However, DL models have received a lot of criticism - especially in time-series forecasting. Since I work with time series, I made an extensive research on the topic, using reliable data and sources from both academia ...
Mar 22, 2024 · If you are just looking to test a bunch of different models quickly for a baseline, like ARIMA, Prophet, LSTM, Croston, etc, I would recommend the darts package. It is like scikit-learn for time series - has a wrapper around every algorithm that exists, even regression algorithms and statistical ...
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May 11, 2023 · Hi, I need some help for my thesis project. I am planning to build a ANN to predict prices of american-options (financial derivatives). In my case i have access to historic data of TSLA, more specific, 2013-2023 data based on 1 minute frequency of all the option chain. There is around 5 TB of data.
[D] Do We Really Need Deep Learning Models for Time Series ...
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Mar 9, 2022 · In a comparative study, a team from University of Hildesheim Germany demonstrated that simple GBRT model (Gradient Boosting Regression Tree) with appropriate features engineering outperform almost all state-of-the-art DNN models evaluated on 9 Time Series Forecasting tasks.
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May 13, 2023 · In recent years, Deep Learning has made remarkable progress in the field of NLP. However, DL models have received a lot of criticism - especially in time-series forecasting. Since I work with time series, I made an extensive research on the topic, using reliable data and sources from both academia ...
May 24, 2022 · [D] Recent research and methods for time series forecasting · N-HiTS · N-BEATS · SCINet (Univariate) · Temporal Fusion Transformer · LightGBM.
Mar 18, 2024 · One thing I definitely wouldn't recommend are pretrained models, time series transformers, LLMs etc. Maaaaaybe TFT, but it's always a mixed bag with deep learning on time series. ... time point prediction (causal model). Huggingface also supports a variety of base models. Though most of this is ...
Feb 2, 2019 · [D] What are some modern machine learning approaches for Time Series Analysis/Forecasting? · Classification · Clustering · Anomaly detection · Rule Discovery · Segmentation · Summarization · Repeated pattern discovery (motif discovery).
Feb 4, 2024 · I wrote a literature review on recent literature applying deep learning to time series forecasting in 2024. I examine recent advances such as more powerful transformer architectures and normalization techniques and if they can beat simple models like D-Linear and N-Linear.
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