Self-Adaptive Scale Handling for Forecasting Time Series with Scale Heterogeneity
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Computer Science > Machine Learning
Title:Self-Adaptive Scale Handling for Forecasting Time Series with Scale Heterogeneity
Abstract:Current time series forecasting (TSF) research predominantly focuses on scale-homogeneous data, where different time series share similar numerical magnitude ranges. However, in real-world industrial scenarios such as financial product sales, different time series often differ by orders of magnitude (scale heterogeneity). Since these series share similar temporal patterns, joint modeling is desirable for better data utilization, yet existing scaling methods either compress low-scale signals (global normalization) or destroy semantic discriminability and amplify inverse-scaling errors (window-based scaling). This paper proposes a self-Adaptive Scale-handling (AS) module that learns adaptive scale factors tailored to each input, preserving semantic discriminability while reducing inverse-scaling errors. AS consists of Scale Calibrating (SC), which calibrates prior mean scaling factors through neural networks, and Scaling Selection (SS), which decides whether to apply calibration or retain the original factor, avoiding over-calibration. Experiments on real-world fund sales datasets from Ant Fortune and Alipay show that AS seamlessly integrates into popular TSF models and consistently improves their performance. The code and dataset are available at the link this https URL.
| Comments: | This is the full version of the paper accepted by the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). The code and dataset are available at this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.20010 [cs.LG] |
| (or arXiv:2606.20010v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.20010
arXiv-issued DOI via DataCite (pending registration)
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