Summary of A Scalable and Transferable Time Series Prediction Framework For Demand Forecasting, by Young-jin Park et al.
A Scalable and Transferable Time Series Prediction Framework for Demand Forecasting
by Young-Jin Park, Donghyun Kim, Frédéric Odermatt, Juho Lee, Kyung-Min Kim
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Forecasting orchestra (Forchestra) framework is a powerful time series forecasting method that can accurately predict future demand for various items. The model’s scalability and expressiveness are improved, enabling it to handle large-scale datasets with up to 0.8 billion parameters. Forchestra outperforms existing models by a significant margin and generalizes well to unseen data points in zero-shot fashion on downstream datasets. The framework is empirically demonstrated to be effective in demand forecasting and logistics optimization tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series forecasting is important for businesses, like predicting demand and optimizing logistics. Traditional methods have limitations because they can’t handle big models while keeping accuracy high. A new method called Forchestra helps fix this problem by being simple yet powerful. It works well with large datasets and even does better than other methods on unseen data. The paper shows how Forchestra compares to other approaches and performs well in real-world scenarios. |
Keywords
* Artificial intelligence * Optimization * Time series * Zero shot
