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, when analyzing time series in which important patterns span longer time windows, such models have difficulty effectively capturing this information. The use of deep networks allows to increase the size of the receptive field and partially solves the problem. But the number of convolutional layers required to cover the entire sequence may be too large, and oversizing the model leads to the vanishing gradient problem.

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