Encoding Seasonal Climate Predictions with Modular Neural Network

Smit Marvaniya; Jitendra Singh; Nicolas Galichet; Fred Ochieng Otieno; Geeth de Mel; Kommy Weldemariam

ICASSP 2024accepted0 citations

Encoding Seasonal Climate Predictions with Modular Neural Network

Smit Marvaniya, Jitendra Singh, Nicolas Galichet, Fred Ochieng Otieno, Geeth de Mel, Kommy Weldemariam

Abstract

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.

BibTeX

@inproceedings{icassp2024_encodingseasonal,
  title = {Encoding Seasonal Climate Predictions with Modular Neural Network},
  author = {Smit Marvaniya and Jitendra Singh and Nicolas Galichet and Fred Ochieng Otieno and Geeth de Mel and Kommy Weldemariam},
  booktitle = {ICASSP 2024},
  year = {2024}
}