AutoML Time Series Forecasting Optimization

The ML model is currently achieving only 72% forecast accuracy, falling short of the 80%+ threshold required for reliable business decision-making. Model selection is a manual and time-consuming process, taking over 40 hours per project without a systematic comparison framework. Performance is particularly weak on seasonal and cyclical patterns, with the model failing to account for factors such as holidays, weather, and promotions. There is no automated retraining in place, leading to model degradation over time as data distributions shift. Additionally, reliance on a single-model approach limits overall performance, with no ensemble strategies implemented in production.