Selection of significant input variables for time series forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "H. D. Tran and N. Muttil and B. J. C. Perera",
  title =        "Selection of significant input variables for time
                 series forecasting",
  journal =      "Environmental Modelling \& Software",
  volume =       "64",
  pages =        "156--163",
  year =         "2015",
  ISSN =         "1364-8152",
  DOI =          "doi:10.1016/j.envsoft.2014.11.018",
  URL =          "",
  abstract =     "Appropriate selection of inputs for time series
                 forecasting models is important because it not only has
                 the potential to improve performance of forecasting
                 models, but also helps reducing cost in data
                 collection. This paper presents an investigation of
                 selection performance of three input selection
                 techniques, which include two model-free techniques,
                 partial linear correlation (PLC) and partial mutual
                 information (PMI) and a model-based technique based on
                 genetic programming (GP). Four hypothetical datasets
                 and two real datasets were used to demonstrate the
                 performance of the three techniques. The results
                 suggested that the model-free PLC technique due to its
                 computational simplicity and the model-based GP
                 technique due to its ability to detect non-linear
                 relationships (demonstrated by its relatively good
                 performance on a hypothetical complex non-linear
                 dataset) are recommended for the input selection task.
                 Candidate inputs which are selected by both these
                 recommended techniques should be considered as
                 significant inputs.",
  keywords =     "genetic algorithms, genetic programming, Time series
                 forecasting, Input variable selection, Partial mutual
                 information, Correlation",

Genetic Programming entries for H D Tran Nitin Muttil B J C Perera