A Hybrid Approach for Time Series Forecasting Using Temporal Convolution Networks and CatBoost Classifier

In retail, demand forecasting is crucial for managing inventory, enhancing efficiency, and ensuring customer satisfaction. Traditional methods often fail to capture complex data patterns, prompting the development of a hybrid model that combines Temporal Convolutional Networks (TCN) and Cat Boost for improved accuracy. Tested on a large dataset of 540,000 demand entries, the model achieved a significant reduction in prediction errors (MSE) by over 96%, enabling retailers to make better decisions regarding stock management, supply chain operations, and customer satisfaction. This approach not only optimizes inventory but also minimizes overstocking and shortages, resulting in enhanced operational efficiency. Such innovative solutions are pivotal for the dynamic and data-driven retail industry.