A Reactive One-Shot Reset Approach for Financial Time Series Forecasting under Concept Drift
DOI:
https://doi.org/10.5753/reic.2026.8438Keywords:
Time Series Forecasting, Financial Market, Concept Drift, LOR, Adaptive ModelsAbstract
Time series forecasting is important for decision-making in the financial market, but it becomes challenging due to volatility and changes in data distribution over time — the so-called concept drift. Approaches designed to handle this drift, especially active ones, often experience performance drops as they rely on large data windows for retraining. To address this, this work proposes LOR (Local One-Shot Reset), a local reset strategy that uses a single observation to adapt to change. Experiments with seven real-world financial time series show that LOR outperforms or performs comparably to traditional state-of-the-art methods designed to handle concept drift.
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