A Scalable and Transferable Time Series Prediction Framework for Demand
  Forecasting
Young-Jin Park Donghyun Kim Frédéric Odermatt Juho Lee Kyung-Min Kim
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 items. We empirically demonstrate that the model size is scalable to
up to 0.8 billion parameters. The proposed method not only outperforms existing
forecasting models with a significant margin, but it could generalize well to
unseen data points when evaluated in a zero-shot fashion on downstream
datasets. Last but not least, we present extensive qualitative and quantitative
studies to analyze how the proposed model outperforms baseline models and
differs from conventional approaches. The original paper was presented as a
full paper at ICDM 2022 and is available at:
https://ieeexplore.ieee.org/document/10027662.