Feature Engineering Methods on Multivariate Time-Series Data for Financial Data Science Competitions
Not on the map. Why?
Abstract (arXiv)
This paper is a work in progress. We are looking for collaborators to provide us financial datasets in Equity/Futures market to conduct more bench-marking studies. The authors have papers employing similar methods applied on the Numerai dataset, which is freely available but obfuscated. We apply different feature engineering methods for time-series to US market price data. The predictive power of models are tested against Numerai-Signals targets.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 15 references that are in the map. First posted on arXiv 26 Mar 2023.
Builds on
8 of 15 references.
References · 8
- 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
- 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 748
- 2019Deep Double Descent: Where Bigger Models and More Data Hurtcited by 667
- 2019catch22: CAnonical Time-series CHaracteristicscited by 103
- 2016A Primer on the Signature Method in Machine Learningcited by 100
- 2020Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPUcited by 45
- 2022Signature Methods in Machine Learningcited by 33
- 2013Highly comparative time-series analysis: The empirical structure of time series and their methodscited by 30
Built on by
Not cited yet.