MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing

2023-02-09 · Zhe Li, Zhongwen Rao, Lujia Pan, Zenglin Xu

Multivariate time series forecasting has been widely used in various practical scenarios. Recently, Transformer-based models have shown significant potential in forecasting tasks due to the capture of long-range dependencies. However, recent studies in the vision and NLP fields show that the role of attention modules is not clear, which can be replaced by other token aggregation operations. This paper investigates the contributions and deficiencies of attention mechanisms on the performance of time series forecasting. Specifically, we find that (1) attention is not necessary for capturing temporal dependencies, (2) the entanglement and redundancy in the capture of temporal and channel interaction affect the forecasting performance, and (3) it is important to model the mapping between the input and the prediction sequence. To this end, we propose MTS-Mixers, which use two factorized modules to capture temporal and channel dependencies. Experimental results on several real-world datasets show that MTS-Mixers outperform existing Transformer-based models with higher efficiency.

📄 PDF Abstract BibTeX arXiv:2302.04501

Code (1)

plumprc/MTS-Mixers 공식 구현 pytorch

Tasks

Multivariate Time Series ForecastingTime SeriesTime Series Analysis Time Series Forecasting

MTS-UNMixers: Multivariate Time Series Forecasting via Channel-Time Dual Unmixing

2024-11-26 · Xuanbing Zhu, Dunbin Shen, Zhongwen Rao, Huiyi Ma 외

Multivariate time series data provide a robust framework for future predictions by leveraging information across multiple dimensions, ensuring broad applicability in practical scenarios. However, their high dimensionalit…

Mamba Multivariate Time Series Forecasting Time Series Time Series Forecasting

Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series

2024-01-08 · Vijay Ekambaram, Arindam Jati, Pankaj Dayama, Sumanta Mukherjee 외

Large pre-trained models excel in zero/few-shot learning for language and vision tasks but face challenges in multivariate time series (TS) forecasting due to diverse data characteristics. Consequently, recent research e…

CPU Few-Shot Learning Time Series Time Series Forecasting+1

DynaConF: Dynamic Forecasting of Non-Stationary Time Series

2022-09-17 · SiQi Liu, Andreas Lehrmann

Deep learning has shown impressive results in a variety of time series forecasting tasks, where modeling the conditional distribution of the future given the past is the essence. However, when this conditional distributi…

Deep Learning Time Series Time Series Analysis Time Series Forecasting

DecoKAN: Interpretable Decomposition for Forecasting Cryptocurrency Market Dynamics

2025-12-23 · Yuan Gao, Zhenguo Dong, Xuelong Wang, Zhiqiang Wang 외 arxiv

Accurate and interpretable forecasting of multivariate time series is crucial for understanding the complex dynamics of cryptocurrency markets in digital asset systems. Advanced deep learning methodologies, particularly …

Time Series Foundation Models for Multivariate Financial Time Series Forecasting

2025-07-09 · Ben A. Marconi arxiv

Financial time series forecasting presents significant challenges due to complex nonlinear relationships, temporal dependencies, variable interdependencies and limited data availability, particularly for tasks involving …

Time Series Forecasting