Summary of Plutus: a Well Pre-trained Large Unified Transformer Can Unveil Financial Time Series Regularities, by Yuanjian Xu et al.
PLUTUS: A Well Pre-trained Large Unified Transformer can Unveil Financial Time Series Regularities
by Yuanjian Xu, Anxian Liu, Jianing Hao, Zhenzhuo Li, Shichang Meng, Guang Zhang
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 paper introduces PLUTUS, a large-scale, transformer-based model that can effectively model high-noise financial time series data. The authors draw inspiration from successful language models in NLP and design an invertible embedding module with contrastive learning and autoencoder techniques to create a one-to-one mapping between raw data and patch embeddings. TimeFormer, an attention-based architecture, is at the core of PLUTUS, enabling it to capture features across both variable and temporal dimensions. The model is pre-trained on an unprecedented dataset of 100 billion observations designed to thrive in noisy financial environments. To our knowledge, PLUTUS is the first open-source, large-scale, pre-trained financial time series model with over one billion parameters. It achieves state-of-the-art performance in various tasks, demonstrating strong transferability and establishing a robust foundational model for finance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new kind of computer program that can understand and predict patterns in financial data. The data is often noisy and hard to work with, but the authors use special techniques to make it easier to analyze. They train their program on an enormous amount of data and show that it performs better than other programs at predicting certain things about finance. This new program could be a valuable tool for people trying to understand and make sense of financial markets. |
Keywords
» Artificial intelligence » Attention » Autoencoder » Embedding » Nlp » Time series » Transferability » Transformer
