Neural Networks in Trading: The Temporal Query Model (TQNet)
The problem is that financial data is not like laboratory measurements taken under sterile conditions. The market has a life of its own: sharp price swings in response to news, periods of high and low volatility, and unexpected correlations that appear and disappear in a matter of hours. Add noise to the mix (measurement errors, quote delays, data gaps), and we are faced with a classic problem: how to extract a reliable signal in a world full of randomness. The situation becomes particularly complex when local relationships between parameters within a single time segment contradict the overall picture across the entire observation history
To bridge this gap : "Temporal Query Network for Efficient Multivariate Time Series Forecasting" proposed a new approach called Temporal Query - a tool that makes it possible to combine local and global views of the market ▶️
#AI #fintech
V/ ( Dmitriy Gizlyk )
mql5.com/en/articles/19157