Abstract
Abstract. Akaike (1974, 1975) has described how canonical variate analysis can be used to identify the structure of linear multivariate time series models. With some modification, the procedure is suitable for finding autoregressive moving average representations which are efficiently parameterized. We describe briefly the method and examine its performance when applied to a well‐known bivariate time series.
Original language | English (US) |
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Pages (from-to) | 153-164 |
Number of pages | 12 |
Journal | Journal of Time Series Analysis |
Volume | 3 |
Issue number | 3 |
DOIs | |
State | Published - May 1982 |
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Statistics, Probability and Uncertainty
- Applied Mathematics
Keywords
- Canonical variate analysis
- mink‐muskrat data