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.
ASJC Scopus subject areas
- Economics and Econometrics