Ister: Inverted Seasonal-Trend Decomposition Transformer for Explainable Multivariate Time Series Forecasting

• URL: http://arxiv.org/abs/2412.18798v2
• Date: Sat, 25 Jan 2025 13:56:24 GMT
• Title: Ister: Inverted Seasonal-Trend Decomposition Transformer for Explainable Multivariate Time Series Forecasting
• Authors: Fanpu Cao, Shu Yang, Zhengjian Chen, Ye Liu, Laizhong Cui,
• Abstract summary: Inverted Seasonal-Trend Decomposition Transformer (Ister)<n>We introduce a novel Dot-attention mechanism that improves interpretability, computational efficiency, and predictive accuracy.<n>Ister enables intuitive visualization of component contributions, shedding lights on model's decision process and enhancing transparency in prediction results.
• Score: 10.32586981170693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In long-term time series forecasting, Transformer-based models have achieved great success, due to its ability to capture long-range dependencies. However, existing models face challenges in identifying critical components for prediction, leading to limited interpretability and suboptimal performance. To address these issues, we propose the Inverted Seasonal-Trend Decomposition Transformer (Ister), a novel Transformer-based model for multivariate time series forecasting. Ister decomposes time series into seasonal and trend components, further modeling multi-periodicity and inter-series dependencies using a Dual Transformer architecture. We introduce a novel Dot-attention mechanism that improves interpretability, computational efficiency, and predictive accuracy. Comprehensive experiments on benchmark datasets demonstrate that Ister outperforms existing state-of-the-art models, achieving up to 10% improvement in MSE. Moreover, Ister enables intuitive visualization of component contributions, shedding lights on model's decision process and enhancing transparency in prediction results.

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