Demand Forecasting with XGBoost and LSTM: An Exploratory Analysis

Predictive models are of great importance to any organization seeking to know and predict demand behavior. With an accurate demand forecast, any business can make plans to prepare, manage inventory, produce goods, and develop logistics systems that will be in place to meet the expected demand. This results in long-term re-investment and growth in operational efficiency and resource management. Effective demand forecasting makes it possible for firms to develop specific and affirmative strategies that reduce costs while improving customer satisfaction. There are two well-known models used in time series analysis: Long Short-Term Memory (LSTM) networks and eXtreme Gradient Boosting (XGBoost). The LSTM is a deep learning architecture that excels in learning and modeling long-range dependencies in a sequence, which is one of the main reasons why it is suitable for this work which deals with predicting demand. XGBoost is a modern and practical implementation of the gradient boosting algorithm that achieves great speed and performance in almost any predictive modeling task. By implementing both LSTM and XGBoost models and analyzing the demand forecasting performance of the two models from key assessment metrics. The two models are evaluated independently against vast quantities of Test Data, which gives a better understanding of the advantages and disadvantages of both approaches. This analysis then provides suggestions regarding the choice of models for forecasting demand within a business where there are several factors to consider, such as the need for explainability, the performance of the model, and the time taken to run the process through looking at results from the individual models.