Delphi Method Supported by Forecasting Software The Delphi method is one of the basic tools for forecasting values in various types of issues. It uses the knowledge of experts, which is properly aggregated (e.g., in the form of descriptive statistics measures) and returns to the previous group of experts again, thus starting the next round of forecasting. The multistage prediction under the Delphi method allows for better stabilization of the results, which is extremely important in the process of forecasting. Experts in the forecasting process often have access to time series forecasting software but do not necessarily use it. Therefore, it seems advisable to add to the aggregate the value obtained using forecasting software. The advantage of this approach is in saving the time and costs of obtaining a forecast. That should be understood as a smaller burden on data analysts and the value of their work. According to the above mentioned key factors, the main contribution of the article is the use of a virtual expert in the form of a computerenhanced mathematical tool, i.e., a programming library for a forecasting time series. The chosen software tool is the Prophet libraryxe2x80x94a Facebook tool that can be used in Python or R programming languages.

Generalizing the Theta method for automatic forecasting Abstract   The Theta method became popular due to its superior performance in the M3 forecasting competition. Since then, although it has been shown that Theta provides accurate forecasts for various types of data, being a solid benchmark to beat, limited research has been conducted to exploit its full potential and generalize its reach. This paper examines three extensions on Thetaxe2x80x99s framework to boost its performance. This includes (i) considering both linear and nonlinear trends, (ii) allowing to adjust the slope of such trends, and (iii) introducing a multiplicative expression of the underlying forecasting model along with the existing, additive one. The proposed modifications transform Theta into a generalized forecasting algorithm, suitable for automatic time series predictions. The proposed algorithm is evaluated using the series of the M, M3, and M4 competitions. Such an evaluation shows that the proposed approach produces more accurate forecasts than the original, classic Theta, both in terms of point forecasts and prediction intervals, and is also more accurate than other wellknown methods for yearly series.