scholarly journals Estimating common trends in multivariate time series using dynamic factor analysis

2003 ◽  
Vol 14 (7) ◽  
pp. 665-685 ◽  
Author(s):  
A. F. Zuur ◽  
R. J. Fryer ◽  
I. T. Jolliffe ◽  
R. Dekker ◽  
J. J. Beukema
Keyword(s):  
Time Series ◽  
Factor Analysis ◽  
Multivariate Time Series ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor ◽  
Common Trends

Smoothed dynamic factor analysis for identifying trends in multivariate time series

2021 ◽  
Author(s):  
Eric J. Ward ◽  
Sean C. Anderson ◽  
Mary E. Hunsicker ◽  
Michael A. Litzow
Keyword(s):  
Time Series ◽  
Factor Analysis ◽  
Multivariate Time Series ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor

Dynamic factor analysis of nonstationary multivariate time series

Psychometrika ◽  
1992 ◽  
Vol 57 (3) ◽  
pp. 333-349 ◽  
Author(s):  
Peter C. M. Molenaar ◽  
Jan G. De Gooijer ◽  
Bernhard Schmitz
Keyword(s):  
Time Series ◽  
Factor Analysis ◽  
Multivariate Time Series ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor

Dynamic factor analysis to estimate common trends in fisheries time series

2003 ◽  
Vol 60 (5) ◽  
pp. 542-552 ◽  
Author(s):  
A F Zuur ◽  
I D Tuck ◽  
N Bailey
Keyword(s):  
Time Series ◽  
Factor Analysis ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor ◽  
Common Trends ◽  
Explanatory Variables ◽  
The North ◽  
The North Atlantic Oscillation ◽  
Aquatic Sciences

Dynamic factor analysis (DFA) is a technique used to detect common patterns in a set of time series and relationships between these series and explanatory variables. Although DFA is used widely in econometric and psychological fields, it has not been used in fisheries and aquatic sciences to the best of our knowledge. To make the technique more widely accessible, an introductory guide for DFA, at an intermediate level, is presented in this paper. A case study is presented. The analysis of 13 landings-per-unit-effort series for Nephrops around northern Europe identified three common trends for 12 of the series, with one series being poorly fitted, but no relationships with the North Atlantic Oscillation (NAO) or sea surface temperature were found. The 12 series could be divided into six groups based on factor loadings from the three trends.

scholarly journals Estimation and classification of temporal trends to support integrated ecosystem assessment

2020 ◽  
Author(s):  
Hiroko Kato Solvang ◽  
Benjamin Planque
Keyword(s):  
Time Series ◽  
Barents Sea ◽  
Multivariate Time Series ◽  
Marine Ecosystem ◽  
Temporal Trends ◽  
Dynamic Factor ◽  
Short Time Series ◽  
Common Trends ◽  
Common Trend ◽  
Short Time

Abstract We propose a trend estimation and classification (TREC) approach to estimating dominant common trends among multivariate time series observations. Our methods are based on two statistical procedures that includes trend modelling and discriminant analysis for classifying similar trend (common trend) classes. We use simulations to evaluate the proposed approach and compare it with a relevant dynamic factor analysis in the time domain, which was recently proposed to estimate common trends in fisheries time series. We apply the TREC approach to the multivariate short time series datasets investigated by the ICES integrated assessment working groups for the Norwegian Sea and the Barents Sea. The proposed approach is robust for application to short time series, and it directly identifies and classifies the dominant trends underlying observations. Based on the classified trend classes, we suggest that communication among stakeholders like marine managers, industry representatives, non-governmental organizations, and governmental agencies can be enhanced by finding the common tendency between a biological community in a marine ecosystem and the environmental factors, as well as by the icons produced by generalizing common trend patterns.

scholarly journals Segmental dynamic factor analysis for time series of curves

2016 ◽  
Vol 27 (6) ◽  
pp. 1617-1637 ◽  
Author(s):  
Allou Samé ◽  
Gérard Govaert
Keyword(s):  
Time Series ◽  
Factor Analysis ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor ◽  
Segmental Dynamic

Using dynamic factor analysis to show how sampling resolution and data gaps affect the recognition of patterns in limnological time series

Inland Waters ◽  
2016 ◽  
Vol 6 (3) ◽  
pp. 284-294 ◽  
Author(s):  
Rosana Aguilera ◽  
David M. Livingstone ◽  
Rafael Marcé ◽  
Eleanor Jennings ◽  
Jaume Piera ◽  
...  
Keyword(s):  
Time Series ◽  
Factor Analysis ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor ◽  
Data Gaps
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