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Forecasting plays an important role in time series analysis. Deep models have brought significant improvement in this area. In addition to successfully predicting future values, they also extract abstract representations that can be applied to other tasks such as classification and anomaly detection.
The Transformer architecture, which originated in the field of natural language processing (NLP), demonstrated its advantages in computer vision (CV) and is successfully applied in time series analysis. Its Self-Attention mechanism, which can automatically identify relationships between elements of a time series, has become the basis for creating effective forecasting models.

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