Adaptive Fine-Tuning via Pattern Specialization for Deep Time Series Forecasting

TL;DR

This paper introduces a novel adaptive framework for deep time series forecasting that specializes models for different regimes and detects concept drift, improving accuracy in non-stationary environments.

Contribution

It proposes a method to fine-tune and select specialized DNN models for different time series regimes, incorporating concept drift detection for better adaptation.

Findings

Significant performance improvements on multiple DNN architectures.

Effective identification and adaptation to evolving patterns.

Generalizable approach applicable across various models.

Abstract

Time series forecasting poses significant challenges in non-stationary environments where underlying patterns evolve over time. In this work, we propose a novel framework that enhances deep neural network (DNN) performance by leveraging specialized model adaptation and selection. Initially, a base DNN is trained offline on historical time series data. A reserved validation subset is then segmented to extract and cluster the most dominant patterns within the series, thereby identifying distinct regimes. For each identified cluster, the base DNN is fine-tuned to produce a specialized version that captures unique pattern characteristics. At inference, the most recent input is matched against the cluster centroids, and the corresponding fine-tuned version is deployed based on the closest similarity measure. Additionally, our approach integrates a concept drift detection mechanism to identify and adapt to emerging patterns caused by non-stationary behavior. The proposed framework is generalizable across various DNN architectures and has demonstrated significant performance gains on both traditional DNNs and recent advanced architectures implemented in the GluonTS library.