Self-Adaptive Scale Handling for Forecasting Time Series with Scale Heterogeneity

2026-06-18 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

The Self-Adaptive Scale-handling (AS) module addresses scale heterogeneity in time series forecasting (TSF), a common issue in industrial scenarios like financial product sales where series differ by orders of magnitude but share temporal patterns. Traditional scaling methods either compress low-scale signals or amplify inverse-scaling errors. AS learns adaptive scale factors for each input, preserving semantic discriminability and reducing inverse-scaling errors. It comprises Scale Calibrating (SC), which uses neural networks to calibrate prior mean scaling factors, and Scaling Selection (SS), which prevents over-calibration by deciding whether to apply calibration or retain the original factor. Experiments on real-world fund sales datasets from Ant Fortune and Alipay demonstrate that AS seamlessly integrates into popular TSF models, consistently improving their performance. The code and dataset are available for further research.

Key takeaway

For Machine Learning Engineers building forecasting models for industrial time series with scale heterogeneity, you should consider integrating the Self-Adaptive Scale-handling (AS) module. This approach directly addresses the limitations of traditional scaling methods by adaptively managing diverse magnitudes, which can significantly improve model performance on datasets like financial product sales. Evaluate AS using the publicly available code and dataset to enhance the accuracy of your existing TSF models.

Key insights

Self-Adaptive Scale-handling (AS) improves time series forecasting by adaptively managing scale heterogeneity without losing signal or amplifying errors.

Principles

• Scale heterogeneity impacts TSF.
• Joint modeling requires adaptive scaling.
• Over-calibration must be avoided.

Method

The AS module calibrates prior mean scaling factors via neural networks (SC) and then selectively applies this calibration or retains original factors (SS) to prevent over-calibration.

In practice

• Integrate AS into existing TSF models.
• Apply AS to financial sales data.
• Utilize provided code and dataset.

Topics

• Time Series Forecasting
• Scale Heterogeneity
• Adaptive Scaling
• Neural Networks
• Financial Data
• Ant Fortune

Code references

• Meteor-Stars/ASTSF

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.