Improving Generalization Capabilities of Models Trained on Time Series Data Using Novel Downsampling Methods

Learning from time series data, particularly univariate time series data (UTSD), is important in various fields. Learning accurately from UTSD is challenging, since the data may be very granular and noisy and thus may contain time points which do not necessarily contribute to, and may even prevent, the ability to attain high generalization capabilities. Downsampling, in which a pared down version of the original UTSD that contains its most valuable parts is preserved, is commonly performed to cope with highly granular or noisy UTSD. However, there is a lack of effective downsampling methods, along with the appropriate evaluation metrics for them. We propose four new UTSD downsampling methods and evaluate their performance on nine commonly used and publicly available datasets. To enable proper evaluation of the downsampling methods, we have proposed three new evaluation metrics. Our evaluation demonstrates that our downsampling methods outperform state-of-the-art (SOT A) methods in two aspects: (1) our methods better preserve the original UTSD; and (2) more importantly, downsampled UTSD provided by our methods allow machine learning (ML) models to attain higher generalization capabilities compared to UTSD that SOTA downsampled. Particularly, our methods obtained better results in almost all the examined datasets (nearly 90%) in comparison to SOTA.