SWIFT: Mapping Sub-series with Wavelet Decomposition Improves Time Series Forecasting
Keywords: Time Series Forecasting, Edge Computing
Abstract: In this paper, we propose $\textit{SWIFT}$, a lightweight model that is not only powerful, but also efficient in deployment and inference for Long-term Time Series Forecasting (LTSF). Our model is based on two key points: 1. decomposition of sequences using wavelet transform. 2. using only one shared single layer for sub-series' mapping. We conduct comprehensive experiments, and the results show that $\textit{SWIFT}$ achieves state-of-the-art (SOTA) performance on multiple datasets, offering a promising method for edge computing and deployment in this task. Moreover, it is noteworthy that the number of parameters in $\textit{SWIFT}$ is only 25\% of what it would be with a single-layer linear model for time-domain prediction.
Primary Area: learning on time series and dynamical systems
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Submission Number: 9920
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