Keywords
Autocorrelation
Fluctuations
Oscillations
Abstract
Time series forecasting is an important analytical tool used in economics, business, environmental studies, and many other fields where understanding temporal patterns is essential for effective planning and decision-making. Among modern forecasting techniques, the Prophet model has gained considerable attention due to its flexibility and ability to model trend and seasonal components in time series data. This study applied Modified Prophet Method (MPM) designed to improve the structural identification capability of the traditional Prophet model. The modification extends the Prophet decomposition framework by introducing an explicit cyclical component alongside the trend, seasonal, and irregular components. The performance of the Modified Prophet Method was compared with the Prophet-style model, Autoregressive Integrated Moving Average (ARIMA), and Exponential Smoothing (ETS). Model performance was assessed using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and residual variance. The results show that the Modified Prophet Method produced the lowest forecasting errors with RMSE of 0.5259, MAE of 0.4221, and residual variance of 0.2789, outperforming the competing models.
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