Suitable Deep Learning Classifier Recommendation for Multi-variate Time Series Classification

Abstract

Time series classification can be categorized into two main types: univariate and multivariate. The major difference between the two is that multivariate datasets have multiple dimensions, which makes them more intricate than univariate datasets. Selecting a suitable classifier for multivariate time series classification can be challenging, as it involves trying out several algorithms to handle the intricate data. While some studies have been conducted on automatically finding suitable classifiers for univariate datasets, there are currently no such studies for multivariate ones. To fill this gap, this paper introduces a recommendation system for deep-learning classifiers that are suitable for multivariate time series classification. The experiments demonstrate that this recommendation system can automatically identify the suitable deep-learning classifier for some multivariate time series datasets.