A Framework for Forecasting Demand of General Time Series Data Using Regression Models and Machine Learning

Accurate demand forecasting is essential for modern business success, as product demand follows various patterns throughout its life cycle and becomes increasingly complex due to consumer fluctuations. This paper presents a statistical demand forecasting framework that integrates both classical and machine learning methods to predict demand patterns across different phases of the product life cycle, focusing on the declining phase. Machine learning techniques are leveraged for their ability to handle complex data patterns. The framework allows each method to be applied individually or combined into an ensemble model. A grid search algorithm is utilized to optimize the weights of each forecasting technique, improving the ensemble model's performance based on the tested data. Validation across five datasets demonstrates the framework's effectiveness, with results showing that the ensemble model outperforms traditional approaches when dealing with mixed demand patterns.