Next Article in Journal
A Multi-Threading Algorithm to Detect and Remove Cycles in Vertex- and Arc-Weighted Digraph
Next Article in Special Issue
Fabric Weave Pattern and Yarn Color Recognition and Classification Using a Deep ELM Network
Previous Article in Journal
Scale Reduction Techniques for Computing Maximum Induced Bicliques
Previous Article in Special Issue
Game Theory-Inspired Evolutionary Algorithm for Global Optimization
Open AccessArticle

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
*
Author to whom correspondence should be addressed.
Algorithms 2017, 10(4), 114; https://doi.org/10.3390/a10040114
Received: 29 July 2017 / Revised: 25 September 2017 / Accepted: 1 October 2017 / Published: 4 October 2017
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-Text
Keywords: ARFIMA; ARMA; machine learning; one-step ahead forecasting; random forests; time series forecasting; variable selection ARFIMA; ARMA; machine learning; one-step ahead forecasting; random forests; time series forecasting; variable selection
Show Figures

Figure 1

MDPI and ACS Style

Tyralis, H.; Papacharalampous, G. Variable Selection in Time Series Forecasting Using Random Forests. Algorithms 2017, 10, 114.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
  • Supplementary File 1:

    Supplementary (ZIP, 3795 KB)

  • Externally hosted supplementary file 1
    Doi: 10.17632/nr3z96jmbm.1
Back to TopTop