Abstract
Long-term time series forecasting has widespread applications in real life, including finance, traffic, weather and sensor data analysis. Time series possess seasonal, trend and irregular components. However, current MLP-based mixing models fall short in effectively capturing multi-scale sequential information and achieving comprehensive feature fusion. The sequence information becomes non-stationary, and as the network depth increases, issues with missing or distorted information flow may arise. In our study, we propose a novel architecture called UNetPlusTS that combines the strengths of Mixer models and Linear models to enhance forecasting capabilities. Specifically, we split series into multiple channels, apply seasonal-trend decomposition to each series and process them independently using our meticulously designed UNet ++ architecture named UNetPlus Mixing Module (UPM). Combined with our unique sampling strategy, it promotes deep integration of seasonal and trend components, thereby alleviating non-stationarity. Experimental results on multiple real-world datasets show that UNetPlusTS has significantly improved the forecasting accuracy, demonstrating its effectiveness and robustness.
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This work is supported by the Joint Innovation Laboratory for Future Observation, Zhejiang University, focusing on the research of cloud-native observability technology based on Guance Cloud (JS20220726-0016).
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Cheng, X., Chen, X., Wu, B., Zou, X., Yang, H., Zhao, R. (2024). UNetPlusTS: Decomposition-Mixing UNet++ Architecture for Long-Term Time Series Forecasting. In: Huang, DS., Zhang, C., Pan, Y. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science(), vol 14876. Springer, Singapore. https://doi.org/10.1007/978-981-97-5666-7_4
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