Transformer-based models demonstrate tremendous potential for Multivariate Time Series (MTS) forecasting due to their ability to capture long-term temporal dependencies by using the self-attention mechanism. However, effectively modeling the spatial correlation cross series for MTS is a challenge for Transformer.
Oct 17, 2024
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Oct 31, 2024 · Transformer-based architectures have achieved remarkable success in natural language processing and computer vision. However, their performance in multivariate ...
Oct 21, 2024 · We present General Time Transformer (GTT), an encoder-only style foundation model for zero-shot multivariate time series forecasting.
Oct 30, 2024 · Abstract:In recent years, Transformer-based models (Transformers) have achieved significant success in multivariate time series forecasting (MTSF).
Oct 23, 2024 · This repo is the official implementation for the paper: TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables. Introduction.
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Oct 30, 2024 · The PatchTSMixer is a lightweight and fast multivariate time series forecasting model with state-of-the-art performance on benchmark datasets.
Oct 30, 2024 · Bi-FI model is designed to simultaneously learn inter and intra-variable features. Bi-FI achieves SOTA performance in three mainstream time series analysis ...
10 hours ago · Abstract—Time series data are pervasive in varied real-world applications, and accurately identifying anomalies in time series is of great importance.
Nov 1, 2024 · Transformer models have revolutionized time series prediction by leveraging their ability to capture long-term dependencies through self-attention ...
Oct 18, 2024 · MTSAD aims to detect time nodes or periods within a multivariate time series that significantly deviate from the established normal pattern. It primarily ...
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