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Price forecasting and market trend prediction are central tasks for successful trading and risk management. High-quality price movement forecasts enable traders to make timely decisions and avoid financial losses. However, in highly volatile markets, traditional machine learning models may be limited in their capabilities.

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The idea was to study a lot of historical data using an algorithm that would find repeating patterns and evaluate their performance. Sounds interesting? In reality, the implementation turned out to be not so simple.

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Let's sum it up. We have created a system that looks for arbitrage opportunities. Remember that the system does not solve all your financial problems.

We have sorted out backtesting. It works with time-based data, and even better, it allows us to see how our system would have worked in the past.

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In the previous article Neural Network in Practice: Pseudoinverse (II), I discussed the importance of dedicated computational systems and the reasons behind their development. In this new article related to neural networks, we will delve deeper into the subject. Creating material for this stage is no simple task. Despite appearing straightforward, explaining something that often causes significant confusion can be quite challenging.

What will we cover at this stage? In this

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The HiVT method begins by representing the road scene as a collection of vectorized elements. Based on this scene representation, the model hierarchically aggregates spatiotemporal information. The road scene consists of agents and map information. For structured scene representation, vectorized elements are first extracted, including trajectory segments of road agents and lane segments from map data.

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