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A Comparison of Automated Time Series Forecasting Tools for Smart Cities

2022; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-031-16474-3_45

1611-3349

Pedro José Pereira, Nuno Marques da Costa, Margarida Barros, Paulo Cortéz, Dalila Durães, António Silva, José Machado,

Air Quality Monitoring and Forecasting

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.