The first part of this article systematically reviews the Transformer model while highlighting its strengths and limitations.

Aim of this paper is to explore the application of Artificial Neural Networks (ANNs) for financial time series forecasting.

Deep learning has recently risen as a dominant technique in a variety of settings comprising large-scale and high-dimensional data.

This book introduces machine learning methods in finance.

... transformer great again for time series forecasting : channel aligned robust dual transformer ( 2023 ) 37. Yu , H.F. ... Financial time series forecasting using CNN and transformer ( 2023 ) 41. Zhang , L. , et al .: A review of ...

... temporal - relational modeling for stock trend prediction . In : IJCAI ( 2021 ) 18. Wang , J. , Zhang , Y. , Tang , K. , Wu , J. , Xiong , Z .: AlphaStock : a buying - winners- and - selling - losers investment strategy using ...

This is in contrast to the wide spectrum of work done on exotic financial instruments, wherein advanced mathematics is used to construct financial instruments for hedging risks and for investment.In this book, particular aspects of the ...

This book takes the reader beyond the ‘black-box’ approach to neural networks and provides the knowledge that is required for their proper design and use in financial markets forecasting —with an emphasis on futures trading.

Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality.