Summary of Dynamic Long-term Time-series Forecasting Via Meta Transformer Networks, by Muhammad Anwar Ma’sum et al.
Dynamic Long-Term Time-Series Forecasting via Meta Transformer Networks
by Muhammad Anwar Ma’sum, MD Rasel Sarkar, Mahardhika Pratama, Savitha Ramasamy, Sreenatha Anavatti, Lin Liu, Habibullah, Ryszard Kowalczyk
First submitted to arxiv on: 25 Jan 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 This paper proposes Meta-Transformer Networks (MANTRA) to address the challenges of long-term time-series forecasting, such as low computational and memory footprints, and robustness against dynamic learning environments. MANTRA relies on fast and slow learners to learn different aspects of data distributions and adapt quickly to changes. The model uses universal representation transformer layers to produce task-adapted representations with a small number of parameters. Experimental results show that MANTRA outperforms baseline algorithms by at least 3% for both multivariate and univariate settings, using four datasets with varying prediction lengths. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MANTRA is a new way to predict what will happen in the future based on past data. It’s like trying to guess what someone will do tomorrow based on what they’ve done before. This paper shows how MANTRA can be used for long-term forecasting, which means predicting things that might happen weeks, months, or even years from now. The problem with long-term forecasting is that it requires a lot of computer power and memory, and also needs to be able to adapt quickly to new information. MANTRA solves these problems by using two types of learners: fast learners that learn quickly and slow learners that fine-tune their learning. This results in more accurate predictions. |
Keywords
* Artificial intelligence * Time series * Transformer
