1 citations · 2 across the 4 of their papers we have counts for
4 papers
Transformers versus LSTMs for electronic trading
Paul Bilokon, Yitao Qiu
With the rapid development of artificial intelligence, long short term memory (LSTM), one kind of recurrent neural network (RNN), has been widely applied in time series prediction.…
A compendium of data sources for data science, machine learning, and artificial intelligence
Paul Bilokon, Oleksandr Bilokon, Saeed Amen
Recent advances in data science, machine learning, and artificial intelligence, such as the emergence of large language models, are leading to an increasing demand for data that ca…
Exploring the Advantages of Transformers for High-Frequency Trading
Fazl Barez, Paul Bilokon, Arthur Gervais +1
This paper explores the novel deep learning Transformers architectures for high-frequency Bitcoin-USDT log-return forecasting and compares them to the traditional Long Short-Term M…
Market regime classification with signatures
Paul Bilokon, Antoine Jacquier, Conor McIndoe
We provide a data-driven algorithm to classify market regimes for time series. We utilise the path signature, encoding time series into easy-to-describe objects, and provide a metr…