Biased Resampling Strategies for Imbalanced Spatio-Temporal Forecasting

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Oliveira, M (Author) Other; Moniz, N (Author) Other; Torgo, L (Author) FCUP

Conference proceedings International

Title: 2019 IEEE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2019)

Pages: 100-109

6th IEEE International Conference on Data Science and Advanced Analytics (DSAA)

Washington, DC, OCT 05-08, 2019

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 4 Citations

Other information

Authenticus ID: P-00R-KHW

DOI: 10.1109/dsaa.2019.00024

Abstract (EN): Extreme and rare events, such as abnormal spikes in air pollution or weather conditions can have serious repercussions. Many of these sorts of events develop from spatio-temporal processes, and accurate predictions are a most valuable tool in addressing their impact, in a timely manner. In this paper, we propose a new set of resampling strategies for imbalanced spatiotemporal forecasting tasks, by introducing bias into formerly random processes. This spatio-temporal bias includes a hyperparameter that regulates the relative importance of the temporal and spatial dimensions in the selection of observations during under- or over-sampling. We test and compare our proposals against standard versions of the strategies on 10 different georeferenced numeric time series, using 3 distinct off-the-shelf learning algorithms. Experimental results show that our proposal provides an advantage over random resampling strategies in imbalanced spatio-temporal forecasting tasks. Additionally, we also find that valuing an observation's recency is more useful when over-sampling; while valuing its spatial distance to other cases with extreme values is more beneficial when under-sampling.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10