MV-kWNN: A novel multivariate and multi-output weighted nearest neighbours algorithm for big data time series forecasting

Abstract

This paper introduces a novel algorithm for big data time series forecasting. Its main novelty lies in its ability to deal with multivariate data, i.e. to consider multiple time series simultaneously, in order to make multi-output predictions. Real-world processes are typically characterised by several interrelated variables, and the future occurrence of certain time series cannot be explained without understanding the influence that other time series might have on the target time series. One key issue in the context of the multivariate analysis is to determine a priori whether exogenous variables must be included in the model or not. To deal with this, a correlation analysis is used to find a minimum correlation threshold that an exogenous time series must exhibit, in order to be beneficial. Furthermore, the proposed approach has been specifically designed to be used in the context of big data, thus making it possible to efficiently process very large time series. To evaluate the performance of the proposed approach we use data from Spanish electricity prices. Results have been compared to other multivariate approaches showing remarkable improvements both in terms of accuracy and execution time.