Time series classification with random temporal features

Abstract

Time series classification exists in widespread domains such as EEG/ECG classification, device anomaly detection, and speaker authentication. Although many methods have been proposed, efficient selection of intuitive temporal features to accurately classify time series remains challenging. Therefore, this paper presents TSC-RTF, a new time series classification method using random temporal features. First, to ensure the intuitiveness of the features, TSC-RTF selects subsequences containing important data points as candidates for intuitive temporal features. Then, TSC-RTF uses random sampling to reduce the number of candidates significantly. Next, TSC-RTF selects the final temporal features using a random forest to ensure the validity of the final temporal features. Finally, a deep learning classifier is trained by TSC-RTF to achieve high accuracy. The experimental results show that the proposed method can compete with the state-of-the-art methods.

Keywords

• Time series classification
• Random feature
• Temporal feature
• Feature selection
• Feature importance measures