Optimizing Retail Forecasting: Long-Term Model Selection with Deep Learning

Retail forecasting requires precise algorithm selection to manage dynamic demand patterns. A single model cannot fit all situations, and the best model may change over time, yet current methods are often slow and impractical at scale. We introduce TimeSpeaks, a deep learning-based framework designed for scalable model selection in retail forecasting. Emphasizing long-term performance, TimeSpeaks dynamically adapts to evolving data patterns, enhancing both scalability and adaptability. Validated using two datasets from a major global retailer, TimeSpeaks consistently outperforms traditional methods across various scenarios. Its ability to adjust to changing data patterns and its reduced dependence on historical data make TimeSpeaks an effective tool for the dynamic needs of retail forecasting.