Algorithms 2017, 10(4), 114; https://doi.org/10.3390/a10040114
Variable Selection in Time Series Forecasting Using Random Forests
Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Iroon Polytechniou 5, 157 80 Zografou, Greece
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Received: 29 July 2017 / Revised: 25 September 2017 / Accepted: 1 October 2017 / Published: 4 October 2017
(This article belongs to the Special Issue Computational Intelligence and Nature-Inspired Algorithms for Real-World Data Analytics and Pattern Recognition)
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. View Full-TextKeywords:
ARFIMA; ARMA; machine learning; one-step ahead forecasting; random forests; time series forecasting; variable selection
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Doi: 10.17632/nr3z96jmbm.1
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Tyralis, H.; Papacharalampous, G. Variable Selection in Time Series Forecasting Using Random Forests. Algorithms 2017, 10, 114.
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Algorithms
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