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Comparing Multistep Ahead Forecasting Functions for Time Series Clustering

  • Marcella CorduasEmail author
  • Giancarlo Ragozini
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

The autoregressive metric between ARIMA processes has been originally introduced as the Euclidean distance between the AR weights of the one-step-ahead forecasting functions. This article proposes a novel distance criterion between time series that compares the corresponding multistep ahead forecasting functions and that relies on the direct method for model estimation. The proposed approach is complemented by a strategy for visual exploration and clustering based on the DISTATIS algorithm.

Keywords

AR metric DISTATIS Time series clustering Multistep forecasting function 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Political SciencesUniversity of Naples Federico IINaplesItaly

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