Abstract:

Forecasting plays a pivotal role in decision-making processes across diverse fields such as economics, business, engineering, and environmental studies. Among the various techniques, exponential-based methods have gained prominence due to their simplicity, adaptability, and efficiency in modeling time-dependent data. This study focuses on the development of a comprehensive exponential methodology for forecasting, with the aim of enhancing predictive accuracy and robustness. The proposed framework integrates traditional exponential smoothing principles with refined parameter optimization techniques to effectively capture short-term fluctuations while accommodating long-term trends and seasonality. Using both simulated and real-world datasets, the performance of the developed exponential methodology is evaluated and compared against conventional forecasting approaches. The results indicate significant improvements in terms of error minimization, scalability, and computational efficiency. This research contributes to the forecasting literature by providing a systematic and adaptable exponential-based methodology that can be applied to a wide spectrum of forecasting problems, thereby supporting informed decision-making and strategic planning.

Description:

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