Abstract
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.
Highlights
There are two different approaches to statistical time series forecasting regarding the exploited information for obtaining the forecasts
The exogenous predictor variables to be utilized for solving a specific forecasting problem could result through large-scale comparisons that precede the application of interest
The fact that the use of these specific exogenous predictor variables did not improve the performance of the algorithms in any of the 513 cases examined should be viewed as a lesson learned from this study
Summary
There are two different approaches to statistical time series forecasting regarding the exploited information for obtaining the forecasts. On the contrary, Hong and Fan (2016) emphasize that the use of appropriate exogenous predictor variables could considerably improve the forecasts. The exogenous predictor variables to be utilized for solving a specific forecasting problem could result through large-scale comparisons (since the results may vary significantly depending on the case study; Papacharalampous et al, 2017b) that precede the application of interest. Such comparisons are known to facilitate benchmarking and model assessment, and require large datasets
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