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This is a discussion on Something to read within the Forex Trading forums, part of the Trading Forum category; The noise prediction module solves the auxiliary problem of identifying noise in the analyzed trajectories. This helps the movement prediction ...

      
   
  1. #451
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    Neural networks made easy (Part 72): Trajectory prediction in noisy environments

    The noise prediction module solves the auxiliary problem of identifying noise in the analyzed trajectories. This helps the movement prediction model better model potential spatial diversity and improves understanding of the underlying representation in movement prediction, thereby improving future predictions.
    The authors of the method conducted additional experiments to empirically demonstrate the critical importance of the spatial consistency and noise prediction modules for SSWNP. When using only the spatial consistency module to solve the movement prediction problem, suboptimal performance of the trained model is observed. Therefore, they integrate both modules in their work.
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    Neural networks made easy (Part 73): AutoBots for predicting price movements

    The proposed method is based on the Encoder-Decoder architecture. It was developed to solve problems of safe control of robotic systems. It allows the generation of sequences of trajectories for multiple agents consistent with the scene. AutoBots can predict the trajectory of one ego-agent or the distribution of future trajectories for all agents in the scene. In our case, we will try to apply the proposed model to generate sequences of price movements of currency pairs consistent with market dynamics.
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    Neural networks made easy (Part 74): Trajectory prediction with adaptation

    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.

    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".
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    Neural networks made easy (Part 75): Improving the performance of trajectory prediction models

    Forecasting the trajectory of the upcoming price movement probably plays one of the key roles in the process of constructing trading plans for the desired planning horizon. The accuracy of such forecasts is critical. In an attempt to improve the quality of trajectory forecasting, we complicate our trajectory forecasting models.
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    Developing a multi-currency Expert Advisor (Part 4): Pending virtual orders and saving status

    In the previous article, we have significantly revised the code architecture to build a multi-currency EA with several parallel working strategies. Trying to achieve simplicity and clarity, we have so far only considered a certain minimum set of functionality. Even considering the limitations of our task, we have significantly altered the code from the previous articles.

    Now hopefully we have the groundwork that is sufficient enough to increase functionality without radical changes to the already written code. We will try to make a minimum number of edits only where it is really necessary.
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    Neural networks made easy (Part 79): Feature Aggregated Queries (FAQ) in the context of state

    Object detection in video has a number of certain characteristics and must solve the problem of changes in object features caused by motion, which are not encountered in the image domain. One of the solutions is to use temporal information and combine features from adjacent frames. The paper "FAQ: Feature Aggregated Queries for Transformer-based Video Object Detectors" proposes a new approach to detecting objects in video. The authors of the article improve the quality of queries for Transformer-based models by aggregating them. To achieve this goal, a practical method is proposed to generate and aggregate queries according to the features of the input frames. Extensive experimental results provided in the paper validate the effectiveness of the proposed method. The proposed approaches can be extended to a wide range of methods for detecting objects in images and videos to improve their efficiency.
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