Impact of Near-Time Information for Prediction on Microeconomic Balanced Time Series Data using Different Machine Learning Methods

Abstract

Instead of relying solely on data of a single time series it is possible to use information of parallel, similar time series to improve prediction quality. Our data set consists of microeconomic data of daily store deposits from a large number of different stores. We analyze how prediction performance regarding a given store can be increased by using data from other stores. First we compare several machine learning methods, including Elastic Nets, Partial Least Squares, Generalized Additive Models, Random Forests, Gradient Boosting and Neural Networks using only data of a single time series. Afterwards we show that Random Forests are able to better utilize parallel time series data compared to Partial Least Squares. Using near-time data of parallel time series is highly beneficial for prediction performance. To allow a fair comparison between different machine learning methods, we present a novel hyper-parameter optimization technique using a regression tree. It enables a fast and flexible determination of optimal parameters for a given method.