A deep learning framework for time series classification using normal cloud representation and convolutional neural network optimization. (28th October 2022)

• Record Type: Journal Article
• Authors: Hssayni, El houssaine; Joudar, Nour‐Eddine; Ettaouil, Mohamed
• Is Part Of: Computational intelligence. Volume 38:Number 6(2022)
• Page Start: 2056
• Page End: 2074
• Publication Date: 2022-10-28
• Subjects: binary optimization -- cloud representation -- convolutional neural networks -- deep learning -- time series classification; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12556
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Time Series Classification (TSC) started getting a lot of attention recently, mostly due to the important real‐world applications of time series such as in the financial industry. In this context, various approaches have been proposed to treat this important and challenging problem in data mining. These approaches include deep learning models that are outperformed the traditional classification techniques. However, the choice of an optimal and efficient deep neural network for TSC is still a major problem. Motivated by this challenge, we propose in this paper, an optimized deep learning framework for TSC tasks, using normal cloud representation and convolutional neural networks optimization (NCR‐CNNO). Our approach consists of two phases: In the first one, we convert the raw time series to a matrix of characteristics using two‐dimensional normal cloud representation. In the second phase, we suggest a CNN to handle the matrix generated in the first phase, as well as, an optimization model is proposed to optimize the unnecessary and redundant parameters. Experiments conducted on extensive time series datasets demonstrate that NCR‐CNNO yields significant improvement in the performance of time series classification compared to the state of the art.