Temporal Fusion Transformer for time series forecasting

Abstract

The ability to accurately forecast sales volumes holds substantial significance for businesses. Current classical models struggle in capturing the impact of different variables upon the sales volume. These machine learning models are also not applicable to more than one specific product. The Temporal Fusion Transformer (TFT) is implemented to address these issues. The TFT is a powerful tool designed for time series forecasting. TFT leverages deep neural networks and self-attention to capture variable dependencies across all time steps,providing temporal context for improved accuracy. The developed TFT model showcases its efficacy in accurate
sales forecasting. By considering both past and future variables, TFT generates predictions with errors of 30%. Moreover, the interpretability of the model highlights the importance of variables such as Covid lockdown periods and product distribution. The scalability of the TFT model allows it to generate forecasts for every product-retailer combination, making it a versatile tool for businesses. As a multi-horizon forecaster, TFT incorporates both past and future variables to generate predictions. This characteristic makes it an excellent candidate for evaluating the impact of changes in future inputs controlled by the business, such as pricing and distribution strategies.