Something to read

In the previous article, we discussed the Conformer method, which was originally developed for weather forecasting. This is quite an interesting method. When testing the trained model, we got a pretty good result. But did we do everything right? Is it possible to get a better result? Let's look at the learning process. We are clearly not using the model forecasting the next most probable timeseries values for its intended purpose. By feeding the model input data from a timeseries, we trained it by propagating the error gradient from models using the prediction results. We started with the Critic's results.

RevIN — is a flexible, trainable layer that can be applied to any arbitrarily chosen layers, effectively suppressing non-stationary information (mean and variance of an instance) in one layer and restoring it in another layer of nearly symmetric position, such as input and output layers.

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Forecasting timeseries is one of the most important elements in building an effective trading strategy. When performing a trading operation in one direction or another, we proceed from our own vision (forecast) of the upcoming price movement. Recent advances in deep learning models, especially architecture-based Transformer models, have demonstrated significant progress in this area, offering a great potential for solving the multifaceted problems associated with long-term timeseries forecasting.

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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 were proposed for solving problems related to timeseries.

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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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