Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification
arXiv:2602.16224v1 Announce Type: new 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) t
arXiv:2602.16224v1 Announce Type: new 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 evolves, and (ii) an amortization model that mitigates predictability estimation errors caused by model bias, further enhancing HPL's effectiveness. The code is available at https://github.com/Meteor-Stars/APTF.
Executive Summary
The proposed Amortized Predictability-aware Training Framework (APTF) addresses the issue of low-predictability samples in time series data, which can lead to training instability and poor model performance. APTF introduces a Hierarchical Predictability-aware Loss (HPL) and an amortization model to identify and penalize low-predictability samples, enabling the model to focus on high-predictability samples. The framework is designed for both time series forecasting and time series classification tasks, and its effectiveness is enhanced by mitigating predictability estimation errors caused by model bias.
Key Points
- ▸ APTF is a general framework for time series forecasting and classification
- ▸ HPL dynamically identifies low-predictability samples and penalizes them
- ▸ Amortization model mitigates predictability estimation errors caused by model bias
Merits
Improved Model Performance
APTF's ability to identify and penalize low-predictability samples can lead to improved model performance and stability
Demerits
Increased Computational Complexity
The introduction of HPL and amortization model may increase the computational complexity of the framework
Expert Commentary
The proposed APTF framework is a significant contribution to the field of time series analysis, as it addresses a critical issue that can affect model performance. The use of HPL and amortization model is a novel approach that can mitigate the adverse effects of low-predictability samples. However, further research is needed to evaluate the framework's performance on different datasets and to explore its potential applications. Additionally, the computational complexity of the framework should be carefully considered to ensure its practicality.
Recommendations
- ✓ Further evaluation of APTF on different datasets and applications
- ✓ Investigation of the framework's computational complexity and potential optimizations
