Summary of Universal Randomised Signatures For Generative Time Series Modelling, by Francesca Biagini et al.
Universal randomised signatures for generative time series modelling
by Francesca Biagini, Lukas Gonon, Niklas Walter
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Mathematical Finance (q-fin.MF); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a generative model for financial time series data using randomized signature and Wasserstein-type distance, inspired by reservoir computing. It introduces a novel metric that captures the distance between conditional distributions and uses it as the loss function in a non-adversarial generator model. The results are compared to benchmarks from existing literature. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to analyze financial data using a mathematical method called randomized signature. It also creates a tool for generating new time series data that looks like real data, but is actually fake. This tool uses a special distance measure that helps it learn how to generate realistic data. The results are compared to what others have done before. |
Keywords
» Artificial intelligence » Generative model » Loss function » Time series
