Forecasting

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In the previous article we got acquainted with the theoretical aspects of the TEMPO method, which proposes an original approach to using pre-trained language models to solve time series forecasting problems. Here's a brief recall of the main innovations of the proposed algorithm.

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In recent years, Transformer-based architectures for multimodal time series forecasting have gained widespread popularity and are progressively becoming one of the most preferred models for time series analysis. Increasingly, models are utilizing independent channel approaches, where the model processes each channel sequence separately, without interacting with the others.

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Object segmentation is a complex scene understanding task aimed not only at detecting objects in a sparse point cloud but also at providing a precise mask for each object.

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