Summary of Mamba4cast: Efficient Zero-shot Time Series Forecasting with State Space Models, by Sathya Kamesh Bhethanabhotla et al.
Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models
by Sathya Kamesh Bhethanabhotla, Omar Swelam, Julien Siems, David Salinas, Frank Hutter
First submitted to arxiv on: 12 Oct 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 Mamba4Cast, a zero-shot foundation model for time series forecasting, generalizes robustly across diverse tasks without fine-tuning. Built on the Mamba architecture and inspired by Prior-data Fitted Networks (PFNs), it achieves strong performance on real-world datasets while reducing inference times compared to transformer-based models. Trained solely on synthetic data, Mamba4Cast generates forecasts for entire horizons in a single pass, outperforming traditional auto-regressive approaches. It performs competitively against state-of-the-art foundation models in various datasets and scales better with prediction length. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mamba4Cast is a new way to predict what will happen next based on past data. This model can do this without needing special training for each specific type of data, which makes it very useful. It’s also much faster than other methods that try to do the same thing. The model was trained using fake data and then tested on real-world datasets. It did really well and is competitive with other top models in this area. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Synthetic data » Time series » Transformer » Zero shot
