Learning Event Representations by Localizing Temporal Reversals

Self-supervised learning has proved useful for time-series classification and forecasting, but common pretraining objectives do not explicitly require features that make individual events easy to recognize and locate. We introduce Temporal Reversal Discrimination (TRD), which trains an encoder to find a short segment reversed within an otherwise unchanged signal. A single linear auxiliary head learns this self-labeled task alongside the original pretraining objective and is discarded afterward, leaving inference unchanged. We evaluate TS2Vec, PatchTST and PMT on heart sounds and wearable operations using frozen features, small labeled training sets and separate labeled validation data. On CirCor heart sounds, linear classifiers fitted on 5% of training patients gain 14–47 points in mean onset macro-F${}_1$ over pretraining without TRD. The benefit persists across the tested classifiers and labeled subsets. On OpenPack, linear classifiers trained on five labeled minutes per training person gain 2–18 points in mean macro-F${}_1$, with mixed results at other settings. PatchTST and PMT also improve on seismic and ECG localization. In the CirCor controls, predicting the reversed positions produces better event features than assigning one reversal label to the window, even when the latter task is learned well. Comparisons with block shuffling show that the better manipulation depends on the encoder and dataset. On CirCor, all three encoders fine-tuned from TRD weights outperform their counterparts fine-tuned after original pretraining or trained from scratch. With the same separately labeled validation cohort, TRD fine-tuning on seven patient groups matches or exceeds the mean onset F${}_1$ of training from scratch on 31 groups.