A Bag of Receptive Fields for Time Series Extrinsic Predictions
Francesco Spinnato Riccardo Guidotti Anna Monreale Mirco Nanni
High-dimensional time series data poses challenges due to its dynamic nature,
varying lengths, and presence of missing values. This kind of data requires
extensive preprocessing, limiting the applicability of existing Time Series
Classification and Time Series Extrinsic Regression techniques. For this
reason, we propose BORF, a Bag-Of-Receptive-Fields model, which incorporates
notions from time series convolution and 1D-SAX to handle univariate and
multivariate time series with varying lengths and missing values. We evaluate
BORF on Time Series Classification and Time Series Extrinsic Regression tasks
using the full UEA and UCR repositories, demonstrating its competitive
performance against state-of-the-art methods. Finally, we outline how this
representation can naturally provide saliency and feature-based explanations.