Encoding Seasonal Climate Predictions with Modular Neural Network

We propose a novel modeling framework that efficiently encodes seasonal climate predictions to provide robust and reliable time-series forecasting for supply chain functions. The encoding framework enables effective learning of latent representations—be it uncertain seasonal climate prediction or other time-series data (e.g., buyer patterns)—via a modular neural network architecture. Our extensive experiments indicate that learning such representations to model seasonal climate forecasts results in an error reduction of approximately 13% to 17% across multiple real-world data sets compared to existing demand forecasting methods.