A brute force tuning of training length for concept drift

Abstract

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We present a brute-force approach to analyze the concept drift behind time sequence data. This approach, named SELECT, searches for the optimal length of training data to minimize error metrics. In other words, SELECT searches for the start point of a new concept from the input sequence. Unlike many related methods, SELECT does not require a pre-specified error threshold to detect drift. In addition, the visual analysis obtained from SELECT enables us to understand how significant a drift has occurred. We test SELECT on two real-world datasets, stock price and COVID-19 infection data. The experimental results show that SELECT can improve the model performance of both datasets. In addition, the visual analysis shows the points of significant drifts, e.g., Lehman’s collapse in stock price data and the spread of variants in COVID-19 data. These results show the effectiveness of the brute-force approach in analyzing concept drift.

Keywords

• concept drift
• drift detection
• window strategy
• stock price prediction
• COVID-19