Variable Selection in Time Series Forecasting Using Random Forests

Hristos Tyralis · Georgia Papacharalampous

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Abstract

Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to suggest an optimal set of predictor variables. Furthermore, we compare its performance to benchmarking methods. The first dataset is composed by 16,000 simulated time series from a variety of Autoregressive Fractionally Integrated Moving Average (ARFIMA) models. The second dataset consists of 135 mean annual temperature time series. The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables. This outcome could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy.

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References

2006Another look at measures of forecast accuracy 2008Automatic Time Series Forecasting: The forecast Package for R 2010To Explain or to Predict?2000The M3-Competition: results, conclusions and implications 200625 years of time series forecasting 2008Forecasting with Exponential Smoothing 2010A novel hybridization of artificial neural networks and ARIMA models for time series forecasting 2012A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition

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