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Advances in Geosciences An open-access journal for refereed proceedings and special publications
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Volume 45 | Copyright
Adv. Geosci., 45, 147-153, 2018
https://doi.org/10.5194/adgeo-45-147-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  17 Aug 2018

17 Aug 2018

Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow

Hristos Tyralis1,* and Georgia A. Papacharalampous2,* Hristos Tyralis and Georgia A. Papacharalampous
  • 1Air Force Support Command, Hellenic Air Force, Elefsina, 192 00, Greece
  • 2Department of Water Resources and Environmental Engineering, National Technical University of Athens, Zografou, 157 80, Greece
  • *These authors contributed equally to this work.

Abstract. We assess the performance of the recently introduced Prophet model in multi-step ahead forecasting of monthly streamflow by using a large dataset. Our aim is to compare the results derived through two different approaches. The first approach uses past information about the time series to be forecasted only (standard approach), while the second approach uses exogenous predictor variables alongside with the use of the endogenous ones. The additional information used in the fitting and forecasting processes includes monthly precipitation and/or temperature time series, and their forecasts respectively. Specifically, the exploited exogenous (observed or forecasted) information considered at each time step exclusively concerns the time of interest. The algorithms based on the Prophet model are in total four. Their forecasts are also compared with those obtained using two classical algorithms and two benchmarks. The comparison is performed in terms of four metrics. The findings suggest that the compared approaches are equally useful.

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We use the CAMELS dataset to compare two different approaches in multi-step ahead forecasting of monthly streamflow. The first approach uses past monthly streamflow information only, while the second approach additionally uses past information about monthly precipitation and/or temperature (exogenous information). The incorporation of exogenous information is made by utilizing Prophet, a model largely implemented in Facebook. The findings suggest that the compared approaches are equally useful.
We use the CAMELS dataset to compare two different approaches in multi-step ahead forecasting of...
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