A Scalable and Transferable Time Series Prediction Framework for Demand Forecasting

arXiv:2402.19402v1 Announce Type: new
Abstract: Time series forecasting is one of the most essential and ubiquitous tasks in many business problems, including demand forecasting and logistics optimization. Traditional time series forecasting methods, however, have resulted in small models with limited expressive power because they have difficulty in scaling their model size up while maintaining high accuracy. In this paper, we propose Forecasting orchestra (Forchestra), a simple but powerful framework capable of accurately predicting future demand for a diverse range of …

arxiv cs.ai cs.lg demand demand forecasting forecasting framework prediction scalable series time series type