TimePro: Efficient Multivariate Long-term Time Series Forecasting with Variable- and Time-Aware Hyper-state

• URL: http://arxiv.org/abs/2505.20774v1
• Date: Tue, 27 May 2025 06:24:21 GMT
• Title: TimePro: Efficient Multivariate Long-term Time Series Forecasting with Variable- and Time-Aware Hyper-state
• Authors: Xiaowen Ma, Zhenliang Ni, Shuai Xiao, Xinghao Chen,
• Abstract summary: In long-term time series forecasting, different variables often influence the target variable over distinct time intervals.<n>Traditional models typically process all variables or time points uniformly, which limits their ability to capture complex variable relationships.<n>We propose TimePro, an innovative Mamba-based model that constructs variate- and time-aware hyper-states.
• Score: 12.940694192516059
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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