Comparison between support vector machines and K-nearest neighbor for time series forecasting

Haitham Fawzy, El Houssainy A. Rady, Amal Mohamed Abdel Fattah

Abstract


This paper aims to use the Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) for univariate time series prediction. The main goal of this study is to compare between Support Vector Machine and K-Nearest Neighbor to predict time series data. The dataset for the monthly gold prices was used during the period from Nov -1989 – Dec-2019, which represents 362 observations. SVM and K-NN models were fitted based on 90% of data as training set, and then their accuracy was compared using the statistical measure RMSE. The results indicated that SVM was better than K-NN in predicting future gold prices, based on RMSE= 33.77.

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Published: 2020-09-07

How to Cite this Article:

Haitham Fawzy, El Houssainy A. Rady, Amal Mohamed Abdel Fattah, Comparison between support vector machines and K-nearest neighbor for time series forecasting, J. Math. Comput. Sci., 10 (2020), 2342-2359

Copyright © 2020 Haitham Fawzy, El Houssainy A. Rady, Amal Mohamed Abdel Fattah. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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