mql5

A common approach when processing time series is to keep the original arrangement of the time steps intact. It is assumed that the historical order is the most optimal. However, most existing models lack explicit mechanisms to explore the relationships between distant segments within each time series, which may in fact have strong dependencies. For example, models based on convolutional networks (CNN) used for time series learning can only capture patterns within a limited time window. As a result,

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Long-term forecasting of time series is a long-standing problem in solving various applied problems. Transformer-based models show promising results. However, high computational complexity and memory requirements make it difficult to use the Transformer for modeling long sequences. This has given rise to numerous studies devoted to reducing computational costs of the Transformer algorithm.
Despite the progress made by Transformer-based time series forecasting methods based, in some cases

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Having already studied the fundamentals of MQL5, you are now prepared to take on one of the most important tasks associated with algorithmic trading: creating a working Expert Advisor. As I indicated in the previous article, We will use a project-based approach for this series. This method helps in both comprehending abstract ideas and recognizing how they are used in practical situations. You will have a firm grasp of how to automate trading decisions based on candlestick patterns and predetermined

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In the previous article "Matrix Factorization: The Basics", I talked a little about how you, my dear readers, can use matrices in your general calculations. However, at that time I wanted you to understand how the calculations were done, so I didn't pay much attention to creating the correct model of the matrices.

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Forecasting long-term timeseries is of specifically great importance for trading. The Transformer architecture, which was introduced in 2017, has demonstrated impressive performance in the areas of Natural Language Processing (NLP) and Computer Vision (CV). The use of Self-Attention mechanisms allows the effective capturing of dependencies over long time intervals, extracting key information from the context. Naturally, quite quickly a large number of different algorithms based on this mechanism

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