Discussing the article: "Neural networks made easy (Part 72): Trajectory prediction in noisy environments"

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Check out the new article: Neural networks made easy (Part 72): Trajectory prediction in noisy environments.

The quality of future state predictions plays an important role in the Goal-Conditioned Predictive Coding method, which we discussed in the previous article. In this article I want to introduce you to an algorithm that can significantly improve the prediction quality in stochastic environments, such as financial markets.

Predicting the future movement of an asset by analyzing its historical trajectories is important in the context of financial market trading, where analysis of past trends can be a key factor for a successful strategy. Future asset trajectories often contain uncertainty due to changes in underlying factors and the market's reaction to them, which determines many potential future asset movements. Therefore, an effective method for predicting market movements must be able to generate a distribution of potential future trajectories, or at least several plausible scenarios.

Despite the considerable variety of existing architectures for the most likely predictions, models can face the problem of overly simplistic forecasts when predicting the future trajectories of financial assets. The problem persists because the model narrowly interprets the data from the training set. In the absence of clear patterns of asset trajectories, the prediction model ends up generating simple or homogeneous movement scenarios that are unable to capture the diversity of changes in the movement of financial instruments. This can lead to a decrease in forecast accuracy.

The authors of the paper "Enhancing Trajectory Prediction through Self-Supervised Waypoint Noise Prediction" offered a new approach to solving these problems, Self-Supervised Waypoint Noise Prediction (SSWNP), which consists of two modules:

• Spatial consistency module
• Noise prediction module

Author: Dmitriy Gizlyk