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

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

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.

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

scholarly journals Delayed phenological timing of dragonfly emergence in Japan over five decades

2008 ◽  
Vol 4 (4) ◽  
pp. 388-391 ◽  
Author(s):  
Hideyuki Doi
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Factor Analysis ◽  
Life Cycle ◽  
Human Population ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor ◽  
Mixing Models ◽  
The Past ◽  
Air Temperatures

Recent increases in air temperature have affected species phenology, resulting in the earlier onset of spring life-cycle events. Trends in the first appearance of adult dragonflies across Japan were analysed using a dataset consisting of observations from 1953 to 2005. Dynamic factor analysis was used to evaluate underlying common trends in a set of 48 time series. The appearance of the first adult dragonfly has significantly shifted to later in the spring in the past five decades. Generalized linear mixing models suggested that this is probably the result of increased air temperatures. Increased summer and autumn temperatures may provide longer bivoltine periods and a faster growth rate; thus, the second generation, which previously hatched in summer, can emerge in the autumn causing the size of the population of dragonflies that emerge in spring to decrease. It is also possible that reduced dragonfly populations along with human development are responsible for a delay in the first observed dragonflies in the spring. However, human population density did not appear to strongly affect the appearance date. This study provides the first evidence of a delay in insect phenological events over recent decades.

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