Solving time series classification problems using support vector machine and neural network
by Mohammed Alweshah; Hasan Rashaideh; Abdelaziz I. Hammouri; Hanadi Tayyeb; Mohammed Ababneh
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 9, No. 3, 2017

Abstract: The major aim of classification is to extract categories of inputs according to their characteristics. The literature contains several methods that aim to solve the time series classification problem, such as the artificial neural network (ANN) and the support vector machine (SVM). Time series classification is a supervised learning method that maps the input to the output using historical data. The primary objective is to discover interesting patterns hidden in the data. In this study, we use a new method called SVNN which combines the SVM and ANN classification techniques to solve the time series data classification problem. The proposed SVNN is applied to six benchmark UCR time series datasets. The results show that the proposed method outperforms the ANN and SVM on all datasets. Further comparison with other approaches in the literature also shows that the SVNN is able to maximise accuracy. It is believed that combining classification techniques can give better results in terms of accuracy and better solutions for time series classification.

Online publication date: Fri, 15-Sep-2017

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