Int. J. Data Analysis Techniques and Strategies, Vol. 9, No. 3, 2017 237 Copyright © 2017 Inderscience Enterprises Ltd. Solving time series classification problems using support vector machine and neural network Mohammed Alweshah*, Hasan Rashaideh, Abdelaziz I. Hammouri, Hanadi Tayyeb and Mohammed Ababneh Prince Abdullah Bin Ghazi Faculty of Information Technology, Al-Balqa Applied University, Salt, Jordan Email: weshah2@yahoo.com Email: rashaideh@gmail.com Email: aziz@bau.edu.jo Email: nodymohammed26@yahoo.com Email: ababnahjo@yahoo.com *Corresponding author 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. Keywords: support vector machine; SVM; artificial neural networks; ANNs; time series problems; classification. Reference to this paper should be made as follows: Alweshah, M., Rashaideh, H., Hammouri, A.I., Tayyeb, H. and Ababneh, M. (2017) ‘Solving time series classification problems using support vector machine and neural network’, Int. J. Data Analysis Techniques and Strategies, Vol. 9, No. 3, pp.237–247. Biographical notes: Mohammed Alweshah received his BS in Computer Sciences from Al-Mustansiriah University, Iraq in 1993 and his Master degree of Computer Science from Al-Balqa Applied University, Jordan in 2005. He received his PhD degree from the Computer Science Department, School of Information Technology, National University of Malaysia (UKM), Malaysia in 2013. He was working under the supervision of Prof. Salwani Abdullah. Currently, he is an Assistant Professor with the Prince Abullah Bin Ghazi Faculty of Information Technology, Al-Balqa Applied University, AlSalt-Jordan. His research interests meta-heuristic algorithms in optimisation areas that involve different real world applications, such as data mining problems.