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High-dimensional time series data poses challenges due to its dynamic nature,varying lengths, and presence of missing values. This kind of data requiresextensive preprocessing, limiting the applicability of existing Time SeriesClassification and Time Series Extrinsic Regression techniques. For thisreason, we propose BORF, a Bag-Of-Receptive-Fields model, which incorporatesnotions from time series convolution and 1D-SAX to handle univariate andmultivariate time series with varying lengths and missing values. We evaluateBORF on Time Series Classification and Time Series Extrinsic Regression tasksusing the full UEA and UCR repositories, demonstrating its competitiveperformance against state-of-the-art methods. Finally, we outline how thisrepresentation can naturally provide saliency and feature-based explanations.

A Bag of Receptive Fields for Time Series Extrinsic Predictions