Summary of Mts-unmixers: Multivariate Time Series Forecasting Via Channel-time Dual Unmixing, by Xuanbing Zhu et al.
MTS-UNMixers: Multivariate Time Series Forecasting via Channel-Time Dual Unmixing
by Xuanbing Zhu, Dunbin Shen, Zhongwen Rao, Huiyi Ma, Yingguang Hao, Hongyu Wang
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed MTS-UNMixer model is a channel-time dual unmixing network for multivariate time series forecasting that tackles the challenges of high dimensionality and mixing patterns in such data. It decomposes the entire series into critical bases and coefficients across both time and channel dimensions, enabling accurate representation and physical interpretability. The model represents sequences over time as a mixture of trends and cycles, with shared representation coefficients between historical and future time periods. Similarly, sequence over channels can be decomposed into tick-wise bases that characterize channel correlations and are shared across the whole series. Two types of Mamba networks are employed to estimate shared time-dependent coefficients and channel-correlated bases, respectively. Experimental results demonstrate significant performance gains compared to existing methods on multiple benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to forecast future events by analyzing patterns in data that changes over time and across different topics. This is useful because it helps us understand how different things are related to each other. The method, called MTS-UNMixer, breaks down the data into simpler components that can be understood better. It’s like finding a hidden pattern in a messy room. The researchers tested this method on many datasets and found that it worked much better than previous methods. |
Keywords
» Artificial intelligence » Time series
