Forward and backward forecasting ensembles for the estimation of time series missing data

Tawfik A. Moahmed, Neamat El Gayar, Amir F. Atiya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

The presence of missing data in time series is big impediment to the successful performance of forecasting models, as it leads to a significant reduction of useful data. In this work we propose a multipleimputation-type framework for estimating the missing values of a time series. This framework is based on iterative and successive forward and backward forecasting of the missing values, and constructing ensembles of these forecasts. The iterative nature of the algorithm allows progressive improvement of the forecast accuracy. In addition, the different forward and backward dynamics of the time series provide beneficial diversity for the ensemble. The developed framework is general, and can make use of any underlying machine learning or conventional forecasting model. We have tested the proposed approach on large data sets using linear, as well as nonlinear underlying forecasting models, and show its success.

Original languageEnglish
Title of host publicationArtificial Neural Networks in Pattern Recognition. ANNPR 2014
EditorsNeamat El Gayar, Friedhelm Schwenker, Ching Y. Suen
PublisherSpringer
Pages93-104
Number of pages12
ISBN (Electronic)9783319116563
ISBN (Print)9783319116556
DOIs
Publication statusPublished - 2014
Event6th IAPR TC3 International Workshop on Artificial Neural Networks for Pattern Recognition 2014 - Montreal, Canada
Duration: 6 Oct 20148 Oct 2014

Publication series

NameLecture Notes in Computer Science
Volume8774
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th IAPR TC3 International Workshop on Artificial Neural Networks for Pattern Recognition 2014
Abbreviated titleANNPR 2014
CountryCanada
CityMontreal
Period6/10/148/10/14

Keywords

  • Ensemble prediction
  • Missing data
  • Time series prediction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Moahmed, T. A., Gayar, N. E., & Atiya, A. F. (2014). Forward and backward forecasting ensembles for the estimation of time series missing data. In N. E. Gayar, F. Schwenker, & C. Y. Suen (Eds.), Artificial Neural Networks in Pattern Recognition. ANNPR 2014 (pp. 93-104). (Lecture Notes in Computer Science; Vol. 8774). Springer. https://doi.org/10.1007/978-3-319-11656-3_9