Feature-aware forecasting of large-scale time series data sets

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

Abstract

The Internet of Things (IoT) sparks a revolution in time series forecasting. Traditional techniques forecast time series individually, which becomes unfeasible when the focus changes to thousands of time series exhibiting anomalies like noise and missing values. This work presents CSAR, a technique forecasting a set of time series with only one model, and a feature-aware partitioning applying CSAR on subsets of similar time series. These techniques provide accurate forecasts a hundred times faster than traditional techniques, preparing forecasting for the arising challenges of the IoT era.

Details

Original languageEnglish
Article number3-4
Pages (from-to)157-168
Number of pages12
Journalit-Information Technology
Volume62
Issue number3-4
Publication statusPublished - 27 May 2020
Peer-reviewedYes

External IDs

Scopus 85082064211
ORCID /0000-0001-8107-2775/work/142253446

Keywords

ASJC Scopus subject areas

Keywords

  • Big Data, Data Analytics, IoT, Time Series Forecasting