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A canonical analysis of multiple time series
Abstract
This paper proposes a canonical transformation of a k-dimensional stationary autoregressive process. The components of the transformed process are ordered from least to most predictable. The least predictable components are often nearly white noise which can reflect stable contemporaneous relationships among the original variables. The most predictable can be nearly nonstationary representing the dynamic growth characteristic of the series. The method is illustrated with a series with five variables.
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Citations
Cited by
Johansen, S., & Juselius, K. (1990) · Oxford Bulletin of Economics and Statistics · doi:10.1111/j.1468-0084.1990.mp52002003.x · Cited by 14,051
Modeling Multiple Time Series with Applications
Tiao, G. C., & Box, G. E. P. (1981) · Journal of the American Statistical Association · doi:10.1080/01621459.1981.10477728 · Cited by 626
Self-citationIdentifying cointegration by eigenanalysis
Zhang, R., Robinson, P., & Yao, Q. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2018.1458620 · Cited by 51
Metadata
- Authors
- BOX, G. E. P., & TIAO, G. C.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1977-08-01
- Volume
- 64
- Issue
- 2
- Pages
- 355-365
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2070173743
- Cited by
- 415
- References
- 0
- FWCI
- 4.97
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A canonical analysis of multiple time series. https://app.cochat.ai/papers/10.1093/biomet/64.2.355
- The paper
- BOX, G. E. P., & TIAO, G. C. (1977). A canonical analysis of multiple time series. Biometrika. https://doi.org/10.1093/biomet/64.2.355
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
