Resampling Strategies for Imbalanced Time Series

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Moniz, N (Author) Other; Branco, P (Author) Other; Torgo, L (Author) FCUP

Conference proceedings International

Title: PROCEEDINGS OF 3RD IEEE/ACM INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS, (DSAA 2016)

Pages: 282-291

3rd IEEE/ACM International Conference on Data Science and Advanced Analytics (DSAA)

Montral, CANADA, OCT 17-19, 2016

Indexing

ISI Web of Knowledge - 6 Citations

Scopus - 7 Citations

Other information

Authenticus ID: P-00M-AMA

DOI: 10.1109/dsaa.2016.37

Abstract (EN): Time series forecasting is a challenging task, where the non-stationary characteristics of the data portrays a hard setting for predictive tasks. A common issue is the imbalanced distribution of the target variable, where some intervals are very important to the user but severely underrepresented. Standard regression tools focus on the average behaviour of the data. However, the objective is the opposite in many forecasting tasks involving time series: predicting rare values. A common solution to forecasting tasks with imbalanced data is the use of resampling strategies, which operate on the learning data by changing its distribution in favor of a given bias. The objective of this paper is to provide solutions capable of significantly improving the predictive accuracy of rare cases in forecasting tasks using imbalanced time series data. We extend the application of resampling strategies to the time series context and introduce the concept of temporal and relevance bias in the case selection process of such strategies, presenting new proposals. We evaluate the results of standard regression tools and the use of resampling strategies, with and without bias over 24 time series data sets from 6 different sources. Results show a significant increase in predictive accuracy of rare cases associated with the use of resampling strategies, and the use of biased strategies.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10