Leading Indicators Selection and Forecasting Packaging Consumption in Thailand

Pawitra Nopsuwan

Abstract


Packaging is one of the essential factors in the production and selling process. Knowing the leading indicators and predicting the demand of consumer goods that use a high volume of packaging can help packaging manufacturers plan their production to satisfy consumers' needs and plan production costs effectively. In this research, we present the economic factors that affect the demand for consumer goods in Thailand and predict the production for each product using machine learning approaches. We use model-based selection, f-regression, mutual information, and recursive feature elimination (RFE) for the feature selection. Moreover, for forecasting, we compare the performance of machine learning models that can describe algorithms inside included multiple linear regression, random forest regression, and gradient boosting. The results show that using random forest regression with model-based feature selection gives the best R square score for 29 out of 47 products or more than 60% of the total products.


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