newdigital

Forecasting time series of future prices is critical in various financial market scenarios. Most of the methods that currently exist are based on certain autocorrelation in the data. In other words, we exploit the presence of correlation between time steps that exists both in the input data and in the predicted values.
Among the models gaining popularity are those based on the Transformer architecture that use Self-Attention mechanisms for dynamic autocorrelation estimation. Also, we see an increasing interest in the use of frequency analysis in forecasting models. The representation of the sequence of input data in the frequency domain helps avoid the complexity of describing autocorrelation and improves the efficiency of various models.

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