A novel time series classification for multivariate data using improved deep belief-recurrent neural network with optimal dynamic time warping

Abstract

In the past ten years, data from time series extraction has attracted a lot of attention. Several methods have concentrated on classification problems, where the objective is to identify the labelling of a test period, given labelled training data. Feature-based and Instance-based methods are the two fundamental groups into which time series categorization methodologies may be divided. To categorize time series data, instance-based techniques use similarity data in a nearest-neighbor context. While methods in this category deliver reliable findings, their efficacy suffers when dealing with lengthy and noisy time series. Feature-based approaches, on the other together, extract characteristics to address the shortcomings of instance-based methods; nevertheless, these approaches use predetermined features and might not be effective in all classification issues. This paper seeks to introduce a novel deep learning-based Optimal Dynamic Time Warping (ODTW) paradigm for multimodal time’s series data categorization. This model covers several phases. At initial stage, the standard data is gathered from standard public source. Secondly, ODTW is proposed, where the parameters are optimized by Random Opposition Billiards-Inspired Optimization (RO-BIO) for extracting the most essential information. Finally, the classification is carried out through “Deep Belief Network (DBN) and Recurrent Neural Networks (RNN) termed as Deep Belief-RNN (DB-RNN)”. Finally, the extracted deep features are given to the optimized RNN for attaining the final classified results. The simulation results have resulted in superior classification performance in terms of standard performance measures.

Keywords

• time series classification
• multivariate data
• optimal dynamic time warping
• random opposition billiards-inspired optimization
• deep belief-recurrent neural network