Forecasting time series using principal component analysis with respect to instrumental variables

Two new forecasting methods of time series are introduced. They are both based on a factorial analysis method called spline principal component analysis with respect to instrumental variables (spline PCAIV). The first method is a straightforward application of spline PCAIV while the second one is an...

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Bibliographic Details
Published in:Computational statistics & data analysis 2008, Vol.52 (3), p.1269-1280
Main Authors: Cornillon, P.-A., Imam, W., Matzner-Løber, E.
Format: Article
Language:English
Subjects:
Additive spline
Exact sciences and technology
Forecasting
General topics
Inference from stochastic processes
time series analysis
Mathematics
Multivariate analysis
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in probability and statistics
PCAIV
Probability and statistics
Sciences and techniques of general use
Statistics
Statistics Theory
Time series
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Summary:Two new forecasting methods of time series are introduced. They are both based on a factorial analysis method called spline principal component analysis with respect to instrumental variables (spline PCAIV). The first method is a straightforward application of spline PCAIV while the second one is an adaptation of spline PCAIV. In the modified version, the used criteria according to the unknown value that need to be predicted are differentiated. Those two forecasting methods are shown to be well adapted to time series.
ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2007.06.017