Distributed Lag Transformer based on Time-Variable-Aware Learning for Explainable Multivariate Time Series Forecasting

• URL: http://arxiv.org/abs/2408.16896v2
• Date: Wed, 13 Aug 2025 04:58:21 GMT
• Title: Distributed Lag Transformer based on Time-Variable-Aware Learning for Explainable Multivariate Time Series Forecasting
• Authors: Younghwi Kim, Dohee Kim, Joongrock Kim, Sunghyun Sim,
• Abstract summary: Distributed Lag Transformer (DLFormer) is a novel Transformer architecture for explainable and scalable time series models.<n>DLFormer integrates a distributed lag embedding and a time variable aware learning (TVAL) mechanism to structurally model both local and global temporal dependencies.<n>Experiments show that DLFormer achieves predictive accuracy while offering robust, interpretable insights into variable wise and temporal dynamics.
• Score: 4.572747329528556
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Time series data is a key element of big data analytics, commonly found in domains such as finance, healthcare, climate forecasting, and transportation. In large scale real world settings, such data is often high dimensional and multivariate, requiring advanced forecasting methods that are both accurate and interpretable. Although Transformer based models perform well in multivariate time series forecasting (MTSF), their lack of explainability limits their use in critical applications. To overcome this, we propose Distributed Lag Transformer (DLFormer), a novel Transformer architecture for explainable and scalable MTSF. DLFormer integrates a distributed lag embedding and a time variable aware learning (TVAL) mechanism to structurally model both local and global temporal dependencies and explicitly capture the influence of past variables on future outcomes. Experiments on ten benchmark and real world datasets show that DLFormer achieves state of the art predictive accuracy while offering robust, interpretable insights into variable wise and temporal dynamics. These results highlight ability of DLFormer to bridge the gap between performance and explainability, making it highly suitable for practical big data forecasting tasks.

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