Recursive multi-step time series forecasting by perturbing data

Souhaib Ben Taieb, Gianluca Bontempi

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

5 Citations (Scopus)

Abstract

The Recursive strategy is the oldest and most intuitive strategy to forecast a time series multiple steps ahead. At the same time, it is well-known that this strategy suffers from the accumulation of errors as long as the forecasting horizon increases. We propose a variant of the Recursive strategy, called RECNOISY, which perturbs the initial dataset at each step of the forecasting process in order to i) handle more properly the estimated values at each forecasting step and ii) decrease the accumulation of errors induced by the Recursive strategy. In addition to the RECNOISY strategy, we propose another strategy, called HYBRID, which for each horizon selects the most accurate approach among the REC and the RECNOISY strategies according to the estimated accuracy. In order to assess the effectiveness of the proposed strategies, we carry out an experimental session based on the 111 times series of the NN5 forecasting competition. Accuracy results are presented together with a paired comparison over the horizons and the time series. The preliminary results show that our proposed approaches are promising in terms of forecasting performance.

Original languageEnglish
Title of host publicationProceedings - 11th IEEE International Conference on Data Mining, ICDM 2011
EditorsDiane Cook, Jian Pei, Wei Wang, Osmar Zaiane, Xindong Wu
Place of PublicationPiscataway NJ USA
PublisherIEEE Computer Society
Pages695-704
Number of pages10
ISBN (Print)9780769544083
DOIs
Publication statusPublished - 2011
Externally publishedYes
EventIEEE International Conference on Data Mining 2011 - Vancouver, Canada
Duration: 11 Dec 201114 Dec 2011
Conference number: 11th
http://icdm2011.cs.ualberta.ca/
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6135855 (Conference Proceedings)

Conference

ConferenceIEEE International Conference on Data Mining 2011
Abbreviated titleICDM 2011
CountryCanada
CityVancouver
Period11/12/1114/12/11
Internet address

Keywords

  • Machine Learning
  • Multi-step forecasting
  • NN5 forecasting competition
  • Recursive forecasting
  • Time series

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