Which time periods does the ROCKET model focus on for each inference?

Abstract

Random Convolutional Kernel Transform (referred to as ROCKET) is an algorithm for learning and inference by a linear model that convolves random kernels on time-series data and uses the maximum value and the proportion of positive values of the inner products obtained for moving windows as the features. In this presentation, we propose a method to calculate for each inference which time periods the ROCKET model focuses on using the regression coefficients of the linear model corresponding to each feature. Numerical experiments using artificial data were conducted to verify the validity of the method. The results show that for some anomaly patterns, it is possible to identify the time period that contributed to the classification.