IndexNet: Timestamp and Variable-Aware Modeling for Time Series Forecasting

• URL: http://arxiv.org/abs/2509.23813v2
• Date: Thu, 02 Oct 2025 07:33:44 GMT
• Title: IndexNet: Timestamp and Variable-Aware Modeling for Time Series Forecasting
• Authors: Beiliang Wu, Peiyuan Liu, Yifan Hu, Luyan Zhang, Ao Hu, Zenglin Xu,
• Abstract summary: IndexNet is a vectors-based augmented framework with an Index Embedding (IE) module.<n>IE transforms timestamps into embedding and injects them into the input sequence, thereby improving the model's ability to capture long-term complex periodic patterns.<n>In parallel, CE assigns each variable a unique and trainable identity embedding based on its index, allowing the model to explicitly distinguish between heterogeneous variables.
• Score: 35.17464235813366
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multivariate time series forecasting (MTSF) plays a vital role in a wide range of real-world applications, such as weather prediction and traffic flow forecasting. Although recent advances have significantly improved the modeling of temporal dynamics and inter-variable dependencies, most existing methods overlook index-related descriptive information, such as timestamps and variable indices, which carry rich contextual semantics. To unlock the potential of such information and take advantage of the lightweight and powerful periodic capture ability of MLP-based architectures, we propose IndexNet, an MLP-based framework augmented with an Index Embedding (IE) module. The IE module consists of two key components: Timestamp Embedding (TE) and Channel Embedding (CE). Specifically, TE transforms timestamps into embedding vectors and injects them into the input sequence, thereby improving the model's ability to capture long-term complex periodic patterns. In parallel, CE assigns each variable a unique and trainable identity embedding based on its index, allowing the model to explicitly distinguish between heterogeneous variables and avoid homogenized predictions when input sequences seem close. Extensive experiments on 12 diverse real-world datasets demonstrate that IndexNet achieves comparable performance across mainstream baselines, validating the effectiveness of our temporally and variably aware design. Moreover, plug-and-play experiments and visualization analyses further reveal that IndexNet exhibits strong generality and interpretability, two aspects that remain underexplored in current MTSF research.

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