Empirical Comparison of Time Series Data Mining Algorithms

Abstract

Time series is a sequence of observed data that is usually ordered in time. Time series data mining is the innovative application of the principles and techniques of data mining in the analysis of time series. This research is aimed to apply data mining techniques to forecasting time series data. Electric Power consumption data consumed by Nigerians from 2001 to 2017. Experiments are conducted with four data mining techniques: Random Regression Forest (RRF), Linear Regression (LR), Support Vector Regression (SVR) and Artificial Neural Network (ANN) which were evaluated based on their forecasting errors generated: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and prediction accuracy on Waikato Environment for Knowledge Analysis (WEKA) platform. The combination of parameters that yields the best results in terms of predefined performance criteria was chosen as optimal for each regressor. A comparative analysis of the regressors’ performance was conducted. All the tested regressors have demonstrated the best prediction quality in short periods of time. SVR demonstrated the best results in terms of both error values and time expenses. Keywords: Time Series Forecast, Time Series Data Mining, Classification Algorithms, Regression analysis, Power consumption