Discussing the article: "Neural networks made easy (Part 74): Trajectory prediction with adaptation"

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Check out the new article: Neural networks made easy (Part 74): Trajectory prediction with adaptation.

This article introduces a fairly effective method of multi-agent trajectory forecasting, which is able to adapt to various environmental conditions.

Building a trading strategy is inseparable from analyzing the market situation and forecasting the most likely movement of a financial instrument. This movement often correlated with other financial assets and macroeconomic indicators. This can be compared with the movement of transport, where each vehicle follows its own individual destination. However, their actions on the road are interconnected to a certain extent and are strictly regulated by traffic rules. Also, due to the individual perception of the road situation by vehicle drivers, a share of stochasticity remains on the roads.

Similarly, in the world of finance, price formation is subject to certain rules. However, the stochasticity of supply and demand created by market participants leads to stochasticity in price. This may be why many trajectory forecasting methods used in the navigation field perform well in predicting future price movements.

In this article I want to introduce you to a method for effectively jointly predicting the trajectories of all agents on the scene with dynamic learning of weights ADAPT, which was proposed to solve problems in the field of navigation of autonomous vehicles. The method was first presented in the article "ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation".

Author: Dmitriy Gizlyk

[Maxim Dmitrievsky]

Dmitry, can you give model testing more attention? Maybe in separate articles. The material is interesting, but it is impossible to draw any conclusions from the given tests. It is also difficult to reproduce (especially for those who don't have GPU or have a macbook at all).