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(3)
Common Trend
Dynamic Factor Analysis
Multivariate Time Series
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Forecasting, Structural Time Series Models and the Kalman Filter
Dynamic factor analysis to estimate common trends in fisheries time series
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Estimating common trends in multivariate time series using dynamic factor analysis
Estimating common trends in multivariate time series using dynamic factor analysis,10.1002/env.611,Environmetrics,A. F. Zuur,R. J. Fryer,I. T. Jolliff
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Estimating common trends in multivariate time series using dynamic factor analysis
(
Citations: 40
)
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A. F. Zuur
,
R. J. Fryer
,
I. T. Jolliffe
,
R. Dekker
,
J. J. Beukema
Journal:
Environmetrics
, vol. 14, no. 7, pp. 665685, 2003
DOI:
10.1002/env.611
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(
doi.wiley.com
)
Citation Context
(16)
...To identify which teleconnection indices might be related to the anchovy/sardine complex, a DFA model (Molenaar, 1985;
Zuur et al., 2003a,
b; Huang et al., 2006) was developed using the time series of the ratio of the catches in each area as response variables, fitting one common trend and using the teleconnection indices and fishing effort as explanatory variables...
Isidora KataraGraham
,
et al.
Environmental drivers of the anchovy/sardine complex in the Eastern Me...
...
2003a
,
2003b
)...
Lori N. Ivan
,
et al.
LongTerm and Interannual Dynamics of Walleye and Yellow Perch in Sagi...
...Popular time series techniques such as spectral analysis, wavelet analysis, ARIMA and BoxJenkins models require stationary time series without missing values, and are not particularly suitable for inferring interrelationships between the data [
1
]...
...Recent advances in computing technologies have encouraged the application of more computationally intensive techniques, such as factor analysis for characterizing multiple time series [
1
], [2],[3]...
...In [
1
] and [3] the authors propose parametric nonBayesian approaches and use Akaike’s information criterion (AIC) for modelselection, whereas [2] proposes a parametric Bayesian approach with a fixed number of factors and MCMC based model inference...
Priyadip Ray
,
et al.
Nonparametric Bayesian factor analysis of multiple time series
...Popular multivariate time series analysis techniques such as spectral methods, wavelet analysis and BoxJenkins models require stationary time series without missing data, and are not particularly suitable for infering interrelationships across data [
1
]...
...As seen from (13) and (18), the primary difference between a stickbreaking prior and a kernel stickbreaking prior is that the stick weights are modulated by an additional bounded kernel K(r;r � ,�l) → [0,
1
] which is a function of the spatial location r. Thus if two cities are spatially proximate, they will have similar stick weights wj(r) and hence are likely to share the same GP...
Priyadip Ray
,
et al.
Nonparametric Bayesian modeling and fusion of spatiotemporal informa...
...With DFA, underlying temporal variation in observed data (response variables) is modeled as linear combinations of common trends (unexplained variability), a constant level parameter, zero or more explanatory variables (additional observed time series), and noise [
Zuur et al., 2003b
]...
...Additionally, Akaike’s information criterion, AIC [Akaike, 1974] is often used as a decision tool for choosing between competing models [
Zuur et al., 2003b
]...
...3.2. Dynamic Factor Analysis [14] DFA is based on structural time series models [Harvey, 1989] and aims to describe a set of N time series (termed response variables) using a dynamic factor model (DFM) that includes M common trends (M < N), K explanatory variables, a level or intercept parameter, and noise [Lütkepohl, 1991;
Zuur et al., 2003b
]:...
...The Kalman filter and smoothing algorithm then skips these missing observations [
Zuur et al., 2003b
]...
...This allowed us to compare the relative importance of explanatory variables across the set of response variables [
Zuur et al., 2003b;
Zuur and Pierce, 2004]...
D. Kaplan
,
et al.
Untangling complex shallow groundwater dynamics in the floodplain wetl...
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