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.
Recommended Citation
Akouemo Kengmo Kenfack, Hermine Nathalie and Povinelli, Richard J., "Time Series Outlier Detection and Imputation" (2014). Electrical and Computer Engineering Faculty Research and Publications. 94.
https://epublications.marquette.edu/electric_fac/94
Comments
Accepted version. Published as part of the proceedings of the conference, 2014 IEEE PES General Meeting, 2014. DOI. © 2014 Institute of Electrical and Electronics Engineers (IEEE). Used with permission.