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

DOI10.1016/j.csda.2007.06.017zbMath1332.62317OpenAlexW2049229442WikidataQ126239895 ScholiaQ126239895MaRDI QIDQ1023449

P.-A. Cornillon, W. Imam, Eric Matzner-Løber

Publication date: 12 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2007.06.017

zbMATH Keywords

time seriesforecastingadditive splinePCAIV

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Cites Work

• Forecasting daily time series using periodic unobserved components time series models
• Forecast comparison of principal component regression and principal covariate regression
• Generalized principal component analysis with respect to instrumental variables via univariate spline transformations
• Nonlinear time series. Nonparametric and parametric methods
• Nonparametric and Non-Linear Models and Data Mining in time Series: A Case-Study on the Canadian Lynx Data
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