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

• URL: http://arxiv.org/abs/2508.07927v1
• Date: Mon, 11 Aug 2025 12:40:08 GMT
• Title: Adaptive Fine-Tuning via Pattern Specialization for Deep Time Series Forecasting
• Authors: Amal Saadallah, Abdulaziz Al-Ademi,
• Abstract summary: Time series forecasting poses significant challenges in non-stationary environments.<n>We propose a novel framework that enhances deep neural network (DNN) performance by leveraging specialized model adaptation and selection.
• Score: 0.8901073744693314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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