Machine learning models are very sensitive instruments. In this series of articles, we will pay significantly more attention to how the transformations we apply to our data, affects our model's performance. Likewise, our models are also sensitive to how the relationship between the input and the target is conveyed. This means, we may need to create new features from the data we have at hand, in order for our model to effectively learn. more...
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 ...
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