Comparative analysis of the outcomes of differing time series forecasting strategies

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Abstract

Time series forecasting of data from various domains has become an increasingly interesting research subject in recent times. Prediction of future sample values is the main goal of time series forecasting. There are mainly two classes of time series forecasting, namely, single step and multi-step forecasting. There are various machine learning approaches that can be used along with various forecasting strategies which exist, some of which can be utilized in and by time series forecasting. However, inappropriate use of strategies can limit the applicability of the techniques to real world problems. This paper provides a review of time series forecasting, a classification of time series forecasting and also approaches and strategies of time series forecasting. It also demonstrates how inappropriate use of point to point rolling forecast strategy for forecasting could lead to unrealistic outcomes and how a multiple step forecasting strategy called Direct H step strategy could help to overcome this issue. Comparative analysis of two strategies using AutoRegressive Integrated Moving Average (ARIMA) approach are demonstrated.
Original languageEnglish
Title of host publication2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
EditorsYong Liu, Liang Zhao, Guoyong Cai, Guoqing Xiao, Kenli Li, Lipo Wang
Place of PublicationGuilin
PublisherInstitute of Electrical and Electronics Engineers
Pages1964-1968
Number of pages5
ISBN (Electronic)978-1-5386-2165-3
DOIs
Publication statusPublished - 25 Jun 2018

Keywords

  • ARIMA
  • Single Step Forecast Strategy
  • Multi Step forecasting Strategy
  • Time Series Analysis

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