TimePro: Efficient Multivariate Long-term Time Series Forecasting with Variable- and Time-Aware Hyper-state Article Swipe
Xiaowen Ma
,
Zemin Ni
,
Shuai Xiao
,
Xinghao Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2505.20774
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2505.20774
In long-term time series forecasting, different variables often influence the target variable over distinct time intervals, a challenge known as the multi-delay issue. Traditional models typically process all variables or time points uniformly, which limits their ability to capture complex variable relationships and obtain non-trivial time representations. To address this issue, we propose TimePro, an innovative Mamba-based model that constructs variate- and time-aware hyper-states. Unlike conventional approaches that merely transfer plain states across variable or time dimensions, TimePro preserves the fine-grained temporal features of each variate token and adaptively selects the focused time points to tune the plain state. The reconstructed hyper-state can perceive both variable relationships and salient temporal information, which helps the model make accurate forecasting. In experiments, TimePro performs competitively on eight real-world long-term forecasting benchmarks with satisfactory linear complexity. Code is available at https://github.com/xwmaxwma/TimePro.
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- http://arxiv.org/abs/2505.20774
- https://arxiv.org/pdf/2505.20774
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https://doi.org/10.48550/arxiv.2505.20774Digital Object Identifier
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TimePro: Efficient Multivariate Long-term Time Series Forecasting with Variable- and Time-Aware Hyper-stateWork title
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preprintOpenAlex work type
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2025Year of publication
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2025-05-27Full publication date if available
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Xiaowen Ma, Zemin Ni, Shuai Xiao, Xinghao ChenList of authors in order
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https://arxiv.org/abs/2505.20774Publisher landing page
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https://arxiv.org/pdf/2505.20774Direct link to full text PDF
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greenOpen access status per OpenAlex
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0Total citation count in OpenAlex
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