Title

Time Series Outlier Detection and Imputation

Document Type

Conference Proceeding

Language

eng

Publication Date

7-2014

Publisher

Institute of Electrical and Electronics Engineers

Source Publication

2014 IEEE PES General Meeting

Source ISSN

1540-7977

Original Item ID

doi: 10.1109/PESGM.2014.6939802

Abstract

This paper proposed the combination of two statistical techniques for the detection and imputation of outliers in time series data. An autoregressive integrated moving average with exogenous inputs (ARIMAX) model is used to extract the characteristics of the time series and to find the residuals. The outliers are detected by performing hypothesis testing on the extrema of the residuals and the anomalous data are imputed using another ARIMAX model. The process is performed in an iterative way because at the beginning the process, the residuals are contaminated by the anomalies and therefore, the ARIMAX model needs to be re-learned on “cleaner” data at every step. We test the algorithm using both synthetic and real data sets and we present the analysis and comments on those results.

Comments

Published as part of the proceedings of the conference, 2014 IEEE PES General Meeting, 2014. DOI.