Combining endogenous and exogenous variables in a special case of non-parametric time series forecasting model

Abstract

We address a problem of increasing quality of forecasting time series by taking into account the information about exogenous time series. We aim to improve a non-parametric forecasting algorithm that minimizes the convolution of a histogram of time series with the loss function. We propose to adjust the histogram, using mixtures of conditional histograms as a less sparse alternative to multidimensional histogram and in some cases demonstrate the decrease of loss compared to the basic forecasting algorithm. To the extent of our knowledge, such approach to combining endogenous and exogenous time series is original and has not been proposed yet. The suggested method is illustrated with the data from the Russian Railways.