Using robust FPCA to identify outliers in functional time series, with applications to the electricity market
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Using robust FPCA to identify outliers in functional time series, with applications to the electricity marketDate
2016Citation
Vilar, Juan M.; Raña, Paula; Aneiros, Germán. “Using robust FPCA to identify outliers in functional time series, with applications to the electricity market”. SORT-Statistics and Operations Research Transactions, 2016, Vol. 40, Num. 2, pp. 321-348, https://raco.cat/index.php/SORT/article/view/316148
Abstract
[Abstract]: This study proposes two methods for detecting outliers in functional time series. Both methods take dependence in the data into account and are based on robust functional principal component analysis. One method seeks outliers in the series of projections on the first principal component. The other obtains uncontaminated forecasts for each data set and determines that those observations whose residuals have an unusually high norm are considered outliers. A simulation study shows the performance of these proposed procedures and the need to take dependence in the time series into account. Finally, the usefulness of our methodology is illustrated in two real datasets from the electricity market: daily curves of electricity demand and price in mainland Spain, for the year 2012. © 2016, Institut d'Estadistica de Catalunya.
Keywords
Electricity demand and price
Functional data analysis
Functional principal component analysis
Functional time series
Outlier detection
Functional data analysis
Functional principal component analysis
Functional time series
Outlier detection
Description
From February 2013 articles are under a Creative Commons license: CC BY-NC-ND
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Atribución-NoComercial-SinDerivadas 3.0 España
ISSN
1696-2281