2022
Pereira, P. J.; Costa, N.; Barros, M.; Cortez, P.; Durães, D.; Silva, A.; Machado, J.
A Comparison of Automated Time Series Forecasting Tools for Smart Cities Proceedings Article
Em: G., Paiva A. Martins B. Marreiros (Ed.): pp. 551-562, Springer Science and Business Media Deutschland GmbH, 2022, ISSN: 03029743, (cited By 2; Conference of 21st EPIA Conference on Artificial Intelligence, EPIA 2022 ; Conference Date: 31 August 2022 Through 2 September 2022; Conference Code:283109).
Resumo | Links | BibTeX | Etiquetas: Automated machine learning; Automated machines; City traffic; Energy-consumption; Forecasting tools; Key Issues; Machine-learning; Rolling window; Time series forecasting; Times series, Automation; Energy utilization; Forecasting; Smart city; Time series, Machine learning
@inproceedings{Pereira2022551,
title = {A Comparison of Automated Time Series Forecasting Tools for Smart Cities},
author = {P. J. Pereira and N. Costa and M. Barros and P. Cortez and D. Durães and A. Silva and J. Machado},
editor = {Paiva A. Martins B. Marreiros G.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138674457&doi=10.1007%2f978-3-031-16474-3_45&partnerID=40&md5=cc3bc2f19869e8c4a50d3a836654d054},
doi = {10.1007/978-3-031-16474-3_45},
issn = {03029743},
year = {2022},
date = {2022-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {13566 LNAI},
pages = {551-562},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Most smart city sensors generate time series records and forecasting such data can provide valuable insights for citizens and city managers. Within this context, the adoption of Automated Time Series Forecasting (AutoTSF) tools is a key issue, since it facilitates the design and deployment of multiple TSF models. In this work, we adapt and compare eight recent AutoTSF tools (Pmdarima, Prophet, Ludwig, DeepAR, TFT, FEDOT, AutoTs and Sktime) using nine freely available time series that can be related with the smart city concept (e.g., temperature, energy consumption, city traffic). An extensive experimentation was carried out by using a realistic rolling window with several training and testing iterations. Also, the AutoTSF tools were evaluated by considering both the predictive performances and required computational effort. Overall, the FEDOT tool presented the best overall performance. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 2; Conference of 21st EPIA Conference on Artificial Intelligence, EPIA 2022 ; Conference Date: 31 August 2022 Through 2 September 2022; Conference Code:283109},
keywords = {Automated machine learning; Automated machines; City traffic; Energy-consumption; Forecasting tools; Key Issues; Machine-learning; Rolling window; Time series forecasting; Times series, Automation; Energy utilization; Forecasting; Smart city; Time series, Machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Most smart city sensors generate time series records and forecasting such data can provide valuable insights for citizens and city managers. Within this context, the adoption of Automated Time Series Forecasting (AutoTSF) tools is a key issue, since it facilitates the design and deployment of multiple TSF models. In this work, we adapt and compare eight recent AutoTSF tools (Pmdarima, Prophet, Ludwig, DeepAR, TFT, FEDOT, AutoTs and Sktime) using nine freely available time series that can be related with the smart city concept (e.g., temperature, energy consumption, city traffic). An extensive experimentation was carried out by using a realistic rolling window with several training and testing iterations. Also, the AutoTSF tools were evaluated by considering both the predictive performances and required computational effort. Overall, the FEDOT tool presented the best overall performance. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.