Time series classification is a supervised machine learning technique used to assign a predefined category or label to an entire sequence of time-ordered data points, rather than predicting a future value. It involves training a model on labeled examples of time series data and then using that model to classify new, unseen time series sequences into their correct classes, which is useful in applications like medical diagnosis, human activity recognition, and sensor data analysis.
Canonical time-series classification benchmark: a suite of 128 univariate time-series datasets (112-dataset subset commonly used for comparison). Methods are ranked by mean accuracy (and mean rank) across all datasets.
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