Encoding Seasonal Climate Predictions for Demand Forecasting with Modular Neural Network

• URL: http://arxiv.org/abs/2309.02248v1
• Date: Tue, 5 Sep 2023 13:58:59 GMT
• Title: Encoding Seasonal Climate Predictions for Demand Forecasting with Modular Neural Network
• Authors: Smit Marvaniya, Jitendra Singh, Nicolas Galichet, Fred Ochieng Otieno, Geeth De Mel, Kommy Weldemariam
• Abstract summary: We propose a novel framework that encodes seasonal climate predictions to provide robust and reliable time-series forecasting for supply chain functions. Our experiments indicate learning such representations to model seasonal climate forecast results in an error reduction of approximately 13% to 17% across multiple real-world data sets.
• Score: 0.8378605337114742
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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