Forecasting Multiple Time Series With One-Sided Dynamic Principal Components Article Swipe
Daniel Peña
,
Ezequiel Smucler
,
Vı́ctor J. Yohai
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1080/01621459.2018.1520117
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1080/01621459.2018.1520117
We define one-sided dynamic principal components (ODPC) for time series as linear combinations of the present and past values of the series that minimize the reconstruction mean squared error. Usually dynamic principal components have been defined as functions of past and future values of the series and therefore they are not appropriate for forecasting purposes. On the contrary, it is shown that the ODPC introduced in this article can be successfully used for forecasting high-dimensional multiple time series. An alternating least-squares algorithm to compute the proposed ODPC is presented. We prove that for stationary and ergodic time series the estimated values converge to their population analogs. We also prove that asymptotically, when both the number of series and the sample size go to infinity, if the data follow a dynamic factor model, the reconstruction obtained with ODPC converges in mean square to the common part of the factor model. The results of a simulation study show that the forecasts obtained with ODPC compare favorably with those obtained using other forecasting methods based on dynamic factor models. Supplementary materials for this article are available online.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1080/01621459.2018.1520117
- OA Status
- green
- Cited By
- 3
- References
- 37
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2749533234
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2749533234Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1080/01621459.2018.1520117Digital Object Identifier
- Title
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Forecasting Multiple Time Series With One-Sided Dynamic Principal ComponentsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-09-11Full publication date if available
- Authors
-
Daniel Peña, Ezequiel Smucler, Vı́ctor J. YohaiList of authors in order
- Landing page
-
https://doi.org/10.1080/01621459.2018.1520117Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://figshare.com/articles/journal_contribution/Forecasting_Multiple_Time_Series_with_One-Sided_Dynamic_Principal_Components/7075103Direct OA link when available
- Concepts
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Series (stratigraphy), Mathematics, Principal component analysis, Applied mathematics, Dynamic factor, Monte Carlo method, Ergodic theory, Factor analysis, Time series, Mean squared error, Statistics, Algorithm, Mathematical analysis, Biology, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2021: 2, 2019: 1Per-year citation counts (last 5 years)
- References (count)
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37Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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