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

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.