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Smart city article details
| Title | A Benchmark Of Automated Multivariate Time Series Forecasting Tools For Smart Cities |
|---|---|
| ID_Doc | 405 |
| Authors | Pereira P.J.; Costa N.; Mestre P.; Cortez P. |
| Year | 2025 |
| Published | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 14969 LNAI |
| DOI | http://dx.doi.org/10.1007/978-3-031-73503-5_12 |
| Abstract | Most Smart Cities data come from multiple related sensors. Within this context, multivariate Automated Time Series Forecasting (AutoTSF) tools are valuable for providing predictive analytics for citizens and city rulers. In this paper, we benchmark seven multivariate open-source AutoTSF tools (AutoARIMAX, AutoGluon, FlaML, AutoTS, MFEDOT and HyperTS) and one univariate AutoTSF tool (FEDOT), measuring both their predictive performances, as well as their computational effort. The tools were evaluated by using four real-world multivariate datasets that were recently collected from a Portuguese city under a realistic rolling window scheme. Overall, the AutoGluon and AutoTS tools presented the best predictive performances, with AutoGluon requiring a substantially reduced training computational effort when compared with AutoTS. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. |
| Author Keywords | Automated Machine Learning; Multivariate Time Series Forecasting; Smart Cities |
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