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[2602.16224] Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification
Summary
The paper presents an Amortized Predictability-aware Training Framework (APTF) designed to enhance time series forecasting and classification by addressing low-predictability samples during model training.
Why It Matters
This research is significant as it tackles the common issue of noise and low-predictability patterns in time series data, which can hinder model performance. By focusing on high-predictability samples, the proposed framework aims to improve training stability and outcomes in critical applications such as finance and weather forecasting.
Key Takeaways
- APTF introduces a Hierarchical Predictability-aware Loss (HPL) to dynamically penalize low-predictability samples.
- The framework enhances model training by focusing on high-predictability data while still learning from low-predictability instances.
- Mitigating predictability estimation errors is crucial for improving model performance in time series tasks.
Computer Science > Machine Learning arXiv:2602.16224 (cs) [Submitted on 18 Feb 2026] Title:Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification Authors:Xu Zhang, Peng Wang, Yichen Li, Wei Wang View a PDF of the paper titled Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification, by Xu Zhang and 3 other authors View PDF HTML (experimental) Abstract:Time series data are prone to noise in various domains, and training samples may contain low-predictability patterns that deviate from the normal data distribution, leading to training instability or convergence to poor local minima. Therefore, mitigating the adverse effects of low-predictability samples is crucial for time series analysis tasks such as time series forecasting (TSF) and time series classification (TSC). While many deep learning models have achieved promising performance, few consider how to identify and penalize low-predictability samples to improve model performance from the training perspective. To fill this gap, we propose a general Amortized Predictability-aware Training Framework (APTF) for both TSF and TSC. APTF introduces two key designs that enable the model to focus on high-predictability samples while still learning appropriately from low-predictability ones: (i) a Hierarchical Predictability-aware Loss (HPL) that dynamically identifies low-predictability samples and progressively expands their loss penalty as training...
