Forecasting Industrial Output in the Philippines: A Geometric Brownian Motion-Neural Network (Gbm-Nn) Model Analysis

This research applies the Geometric Brownian Motion-Neural Network (GBM-NN) model to predict the industrial output in the Philippines from January 1993 to December 2022. The GBM-NN model uses the stochastic properties of Geometric Brownian Motion to estimate trends and volatility better and introduces a neural network for more predictive accuracy based on nonlinearity in the data. The result suggests an industrial production series characterized by high variability. Due to the application of the GBM-NN method, it is verified that this model issues a more coherent prediction than traditional methods due to the lower RMSE and better R-square values. Results show a modest recovery in the industrial sector and further illustrate that the model is an excellent tool to help policymakers and industry participants make better decisions during uncertain economic periods.