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In time-series classification, conventional deep learning methods often treat continuous signals as discrete windows, each analyzed independently without considering the contextual information from adjacent windows. This study introduces a novel, lightweight Bayesian meta-classification approach designed to enhance prediction accuracy by integrating contextual label information from neighboring windows. Alongside training a deep learning model, we construct a Conditional Probability Table (CPT) during training to capture label transitions. During inference, these CPTs are utilized to adjust the predicted class probabilities of each window, taking into account the predictions of preceding windows. Our experimental analysis, focused on Human Activity Recognition (HAR) time series datasets, demonstrates that this approach not only surpasses the baseline performance of standalone deep learning models but also outperforms contemporary state-of-the-art methods that integrate temporal context into time series prediction.
Irani et al. (Sun,) studied this question.