Variable selection in time series forecasting using random forests

DOI10.3390/a10040114zbMath1461.62168OpenAlexW2760894977WikidataQ58461965 ScholiaQ58461965MaRDI QIDQ2633174

Hristos Tyralis, Georgia Papacharalampous

Publication date: 8 May 2019

Published in: Algorithms (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3390/a10040114

zbMATH Keywords

machine learningvariable selectiontime series forecastingARMArandom forestsARFIMAone-step ahead forecasting

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Random graphs (graph-theoretic aspects) (05C80)

Related Items (4)

Random Forest Variable Selection for Sparse Vector Autoregressive Models ⋮ Variable selection in time series forecasting using random forests ⋮ Information model for pricing on electronic markets ⋮ Time series forecasting using a two-level multi-objective genetic algorithm: a case study of maintenance cost data for tunnel fans

Uses Software

• R
• forecast
• reshape
• ggplot2
• Forecast
• caret
• GeneSrF
• ARFIMA
• AppliedPredictiveModeling
• randomForest
• expsmooth
• gdata
• fpp

Cites Work

• Unnamed Item
• Unnamed Item
• To explain or to predict?
• Forecasting with exponential smoothing. The state space approach
• Statistical uncertainty estimation using random forests and its application to drought forecast
• Consistency of random forests
• A random forest guided tour
• Variable selection in time series forecasting using random forests
• Applied Predictive Modeling
• ggplot2
• Forecasting and operational research: a review
• An investigation of neural networks for linear time-series forecasting
• Random forests

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