scholarly journals Seasonal Dynamic Factor Analysis and Bootstrap Inference: Application to Electricity Market Forecasting

Technometrics ◽  
2011 ◽  
Vol 53 (2) ◽  
pp. 137-151 ◽  
Author(s):  
Andrés M. Alonso ◽  
Carolina García-Martos ◽  
Julio Rodríguez ◽  
María Jesús Sánchez
Keyword(s):  
Factor Analysis ◽  
Electricity Market ◽  
Seasonal Dynamic ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor ◽  
Bootstrap Inference

scholarly journals Container Throughput Forecasting Using Dynamic Factor Analysis and ARIMAX Model

2017 ◽  
Vol 29 (5) ◽  
pp. 529-542 ◽  
Author(s):  
Marko Intihar ◽  
Tomaž Kramberger ◽  
Dejan Dragan
Keyword(s):  
Factor Analysis ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Real Data ◽  
Information Criteria ◽  
Dynamic Factor Analysis ◽  
Forecasting Model ◽  
Dynamic Factor ◽  
Macroeconomic Indicators ◽  
The Impact

The paper examines the impact of integration of macroeconomic indicators on the accuracy of container throughput time series forecasting model. For this purpose, a Dynamic factor analysis and AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX) are used. Both methodologies are integrated into a novel four-stage heuristic procedure. Firstly, dynamic factors are extracted from external macroeconomic indicators influencing the observed throughput. Secondly, the family of ARIMAX models of different orders is generated based on the derived factors. In the third stage, the diagnostic and goodness-of-fit testing is applied, which includes statistical criteria such as fit performance, information criteria, and parsimony. Finally, the best model is heuristically selected and tested on the real data of the Port of Koper. The results show that by applying macroeconomic indicators into the forecasting model, more accurate future throughput forecasts can be achieved. The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020). Hence, care must be taken concerning any bigger investment decisions initiated from the management side. It is believed that the proposed model might be a useful reinforcement of the existing forecasting module in the observed port.

Analysis of Power Output Time Series in Response to Supramaximal Exercise: An Approach Through Dynamic Factor Analysis

2011 ◽  
Vol 23 (1) ◽  
pp. 3-16 ◽  
Author(s):  
Paula Marta Bruno ◽  
Fernando Duarte Pereira ◽  
Renato Fernandes ◽  
Gonçalo Vilhena de Mendonça
Keyword(s):  
Factor Analysis ◽  
Power Output ◽  
Soccer Players ◽  
Upper Body ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor ◽  
Lower Body ◽  
Supramaximal Exercise ◽  
Output Time ◽  
Pattern Of Variation

The responses to supramaximal exercise testing have been traditionally analyzed by means of standard parametric and nonparametric statistics. Unfortunately, these statistical approaches do not allow insight into the pattern of variation of a given parameter over time. The purpose of this study was to determine if the application of dynamic factor analysis (DFA) allowed discriminating different patterns of power output (PO), during supramaximal exercise, in two groups of children engaged in competitive sports: swimmers and soccer players. Data derived from Wingate testing were used in this study. Analyses were performed on epochs (30 s) of upper and lower body PO obtained from twenty two healthy boys (11 swimmers and 11 soccer players) age 11–12 years old. DFA revealed two distinct patterns of PO during Wingate. Swimmers tended to attain their peak PO (upper and lower body) earlier than soccer players. As importantly, DFA showed that children with a given pattern of upper body PO tend to perform similarly during lower body exercise.

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

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 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 Models With Time-Varying Parameters

2011 ◽  
Vol 46 (2) ◽  
pp. 303-339 ◽  
Author(s):  
Sy-Miin Chow ◽  
Jiyun Zu ◽  
Kim Shifren ◽  
Guangjian Zhang
Keyword(s):  
Factor Analysis ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor ◽  
Time Varying ◽  
Varying Parameters ◽  
Time Varying Parameters ◽  
Analysis Models

scholarly journals The Role of Environmental Background Processes in Determining Groundwater Level Variability—An Investigation of a Record Flood Event Using Dynamic Factor Analysis

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2336
Author(s):  
Balázs Trásy ◽  
Norbert Magyar ◽  
Tímea Havril ◽  
József Kovács ◽  
Tamás Garamhegyi
Keyword(s):  
Factor Analysis ◽  
Groundwater Level ◽  
Coefficient Of Determination ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor ◽  
Climate Change Scenarios ◽  
Background Factors ◽  
Groundwater Levels ◽  
Groundwater Level Fluctuations ◽  
Environmental Background

Since groundwater is a major source of water for drinking and for industrial and irrigation uses, the identification of the environmental processes determining groundwater level fluctuation is potentially a matter of great consequence, especially in light of the fact that the frequency of extreme climate events may be expected to increase, causing changes in groundwater recharge systems. In the recent study, data measured at a frequency of one hour were collected from the Szigetköz, an inland delta of the Danube. These were then used to determine the presence, or not, and magnitude of any hidden environmental background factors that may be causing groundwater level fluctuations. Through the application of dynamic factor analysis, it was revealed that changes in groundwater level are mainly determined by (i) the water level of neighboring rivers and (ii) evapotranspiration. The intensity of these factors may also be estimated spatially. If the background factors determined by dynamic factor analysis do indeed figure in the linear model as variables, then the time series of groundwater levels can be said to have been accurately estimated with the use of linear regression. The accuracy of the estimate is indicated by the fact that adjusted coefficient of determination exceeds 0.9 in 80% of the wells. The results, via an enhanced understanding of the reasons for changes in the fluctuation of groundwater, could assist in the development of sustainable water management and irrigation strategies and the preparation for varying potential climate change scenarios.

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