ParallelTime: Dynamically Weighting the Balance of Short- and Long-Term Temporal Dependencies

• URL: http://arxiv.org/abs/2507.13998v1
• Date: Fri, 18 Jul 2025 15:08:02 GMT
• Title: ParallelTime: Dynamically Weighting the Balance of Short- and Long-Term Temporal Dependencies
• Authors: Itay Katav, Aryeh Kontorovich,
• Abstract summary: In natural language processing, an approach has been used that combines local window attention for capturing short-term dependencies and Mamba for capturing long-term dependencies.<n>We find that for time-series forecasting tasks, assigning equal weight to long-term and short-term dependencies is not optimal.<n>We propose a dynamic weighting mechanism, ParallelTime Weighter, which calculates interdependent weights for long-term and short-term dependencies.
• Score: 11.40258240052954
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern multivariate time series forecasting primarily relies on two architectures: the Transformer with attention mechanism and Mamba. In natural language processing, an approach has been used that combines local window attention for capturing short-term dependencies and Mamba for capturing long-term dependencies, with their outputs averaged to assign equal weight to both. We find that for time-series forecasting tasks, assigning equal weight to long-term and short-term dependencies is not optimal. To mitigate this, we propose a dynamic weighting mechanism, ParallelTime Weighter, which calculates interdependent weights for long-term and short-term dependencies for each token based on the input and the model's knowledge. Furthermore, we introduce the ParallelTime architecture, which incorporates the ParallelTime Weighter mechanism to deliver state-of-the-art performance across diverse benchmarks. Our architecture demonstrates robustness, achieves lower FLOPs, requires fewer parameters, scales effectively to longer prediction horizons, and significantly outperforms existing methods. These advances highlight a promising path for future developments of parallel Attention-Mamba in time series forecasting. The implementation is readily available at: \href{https://github.com/itay1551/ParallelTime}{ParallelTime GitHub

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