Forecasting Industrial Output in the Philippines: A Geometric Brownian Motion-Neural Network (Gbm-Nn) Model Analysis
Journal Title: International Journal of Social Science Humanity & Management Research - Year 2024, Vol 3, Issue 09
Abstract
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.
Authors and Affiliations
Vicente E. Montano, Rowena C. Cinco
Keywords
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- EP ID EP748297
- DOI 10.58806/ijsshmr.2024.v3i9n14
- Views 53
- Downloads 0
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