A New Methodology Based on Imbalanced Classification for Predicting Outliers in Electricity Demand Time Series

Francisco Javier Duque-Pintor, Manuel Jesús Fernández-Gómez, Alicia Troncoso and Francisco Martínez-Álvarez
Additional contact information
Francisco Javier Duque-Pintor: Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Manuel Jesús Fernández-Gómez: Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Alicia Troncoso: Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Francisco Martínez-Álvarez: Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain

Energies, 2016, vol. 9, issue 9, 1-10

Abstract: The occurrence of outliers in real-world phenomena is quite usual. If these anomalous data are not properly treated, unreliable models can be generated. Many approaches in the literature are focused on a posteriori detection of outliers. However, a new methodology to a priori predict the occurrence of such data is proposed here. Thus, the main goal of this work is to predict the occurrence of outliers in time series, by using, for the first time, imbalanced classification techniques. In this sense, the problem of forecasting outlying data has been transformed into a binary classification problem, in which the positive class represents the occurrence of outliers. Given that the number of outliers is much lower than the number of common values, the resultant classification problem is imbalanced. To create training and test sets, robust statistical methods have been used to detect outliers in both sets. Once the outliers have been detected, the instances of the dataset are labeled accordingly. Namely, if any of the samples composing the next instance are detected as an outlier, the label is set to one. As a study case, the methodology has been tested on electricity demand time series in the Spanish electricity market, in which most of the outliers were properly forecast.

Keywords: time series; imbalanced classification; forecasting; outliers (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/1996-1073/9/9/752/pdf (application/pdf)
https://www.mdpi.com/1996-1073/9/9/752/ (text/html)

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:gam:jeners:v:9:y:2016:i:9:p:752-:d:78211

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().