[2A5-GS-2] Machine learning

[2A5-GS-2-01] Eliciting variable importances of multivariate time series in classification models from feature representation by ROCKET.

〇Takuji Oba¹, Shigeta Naganuma¹, Akihiro Shiozawa¹, Ryoto Yamashita¹ (1. NTT DATA Mathematical Systems Inc.)

Keywords:time series classification, time series analysis, variable importance, feature importance, ROCKET

ROCKET (RandOM Convolutional KERrnel Transform) gains remarkable success to achive comparable performance with other SOTA algorithm in a fraction of the time. ROCKET, a feature-generating algorithm for uni- and multi-variate time series is used in combination with linear classifiers, such as Ridge model. Apart from achieving good classification result, to know the driving factor of the classification is equally important in practice. This corresponds to know which time series variable contribute to the classification result. Usually magnitude of weight coefficients for each feature is used as the measure. But ROCKET `convolves' the original time series variables, and makes it difficult to extract such an information from the classification result. In this paper we propose a method to elicit importance of each original time series variable from weight coefficients of linear classifier with ROCKET features. We show our method can successfully reconstruct the importance of original variables of multivariate time series from the weight coefficients of Ridge classifier applied on the ROCKET-generated features.