Encoding Seasonal Climate Predictions for Demand Forecasting with Modular Neural Network

TL;DR

This paper introduces a modular neural network framework that effectively encodes seasonal climate forecasts to improve demand prediction accuracy in supply chain management, addressing the uncertainty and complexity of climate data.

Contribution

The paper presents a novel encoding framework for seasonal climate predictions using a modular neural network, enhancing demand forecasting accuracy in supply chains.

Findings

Achieved 13-17% reduction in forecasting error across multiple datasets.

Demonstrated robustness of the encoding framework in uncertain climate conditions.

Improved demand prediction by effectively modeling seasonal climate variability.

Abstract

Current time-series forecasting problems use short-term weather attributes as exogenous inputs. However, in specific time-series forecasting solutions (e.g., demand prediction in the supply chain), seasonal climate predictions are crucial to improve its resilience. Representing mid to long-term seasonal climate forecasts is challenging as seasonal climate predictions are uncertain, and encoding spatio-temporal relationship of climate forecasts with demand is complex. 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 forecast results in an error reduction of approximately 13\% to 17\% across multiple real-world data sets compared to existing demand forecasting methods.