Time series forecasting for electricity consumption using kernel principal component analysis (kPCA) and support vector machine (SVM)

Abstract

Time series data are collected based on certain periods which have constants value (e.g. daily, weekly or monthly), it can be used to forecast or predict future circumstance. Prediction is one of the objectives of the time series analysis by identifying the model from previous data and assuming the current information will also occur in the future. In Big Data trend, huge amount of time series data come from different heterogeneous sources and multiple application domains. This present a new challenge for time series forecasting. Time series modelling has been widely applied and proposed in various fields to improve its accuracy and efficiency of forecasting. This paper discusses time series forecasting for electricity consumption using kernel principal component analysis (KPCA) and Support Vector Machine (SVM). This research will be measured with Mean Absolute Error and Mean Squared Error.