Automatic Detection and Imputation of Outliers in Electricity Price Time Series

Ilaria Lucrezia Amerise ()
Additional contact information
Ilaria Lucrezia Amerise: University of Calabria, Department of Economics, Statistics and Finance

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2018, pp 45-49 from Springer

Abstract: Abstract In high frequency time series of electricity prices, one frequently observes a feature which is common for most electricity markets, namely sudden extreme prices. The present study relates to a method for automatically determining and replacing outliers. The core of our method is the construction of a reference time series through the rolling decomposition into trend-cycle and seasonal components of the original time series. Deviations of residuals above a given threshold indicate anomalies which are replaced with a more reasonable alternative price.

Keywords: Time series; Anomalies; Electricity markets (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-89824-7_8

Ordering information: This item can be ordered from
http://www.springer.com/9783319898247

DOI: 10.1007/978-3-319-89824-7_8

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().