Optimal ShortTerm Forecasting Using GABased HoltWinters Method One of the key issues nowadays in using HoltWinters Method of forecasting is the appropriate selection of smoothing coefficients. To identify values of smoothing coefficients, an optimization approach is explored that minimizes a forecasting error like Mean Squared Errors (MSE) or Mean Absolute Deviation (MAD). This paper develops a methodology that optimizes forecasting error by determining the optimal smoothing coefficients of the HoltWinters Method using Genetic Algorithm (GA). This paper focuses on the Mean Square Error (MSE) as an objective value of the optimization problem. The efficiency of the proposed approach was verified using actual test cases based on rice stock commodity in the Philippines. Different variants of the HoltWinters Method were examined and the result shows that additive seasonal effect was more appropriate for the rice stock data. The proposed approach was compared to other models and the results are promising.

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.