Finding common seasonal patterns among time series. An MDS approach

Adi Raveh, Charles Tapiero

Research output: Contribution to journalArticle

Abstract

This paper provides an approach to the analysis of time series seasonal pattern similarities based on a special MDS approach - the non-metric SSA-I (Smallest Space Analysis) technique. Indices of dissimilarity for time series are defined generally while special cases drawn from the economic problems are treated by means of examples. The basic contributions of the paper are two-fold: First we extend the use of SSA-I to time series analysis by transforming the mutual relationship between (as well as within) the time series in a symmetric matrix. As a result, the tool of SSA-I developed by L. Guttman may easily be used. Second, by an introduction of non-metric techniques such as SSA-I in time series analysis we increase our capacity to deal with problems hitherto unsolved. In particular, ordinal data as well as behavioral data for which model processes are not defined and seasonal patterns similarities may be studied by our technique.

Original languageEnglish (US)
Pages (from-to)353-363
Number of pages11
JournalJournal of Econometrics
Volume12
Issue number3
DOIs
StatePublished - 1980

Fingerprint

Time series
Time Series Analysis
Ordinal Data
Dissimilarity
Symmetric matrix
Process Model
Fold
Multidimensional scaling
Economics
Similarity
Time series analysis

ASJC Scopus subject areas

  • Economics and Econometrics
  • Finance
  • Statistics and Probability

Cite this

Finding common seasonal patterns among time series. An MDS approach. / Raveh, Adi; Tapiero, Charles.

In: Journal of Econometrics, Vol. 12, No. 3, 1980, p. 353-363.

Research output: Contribution to journalArticle

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