Madeinfootball Transfermarkt

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Channel independent patch time series transformer works very well for long term forecasting and representation learning

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Promoting openness in scientific communication and the peer review process

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Time series forecasting remains a critical challenge across various domains often complicated by high dimensional data and long term dependencies This paper presents a

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ABSTRACT Time series analysis is of immense importance in extensive applications such as weather forecasting anomaly detection and action recognition This paper focuses on

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Figure 1 TSMixer for multivariate time series forecasting The columns of the inputs means different features variates and the rows are time steps The fully connected operations are row

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Abstract Motivated by recent advances in large language models for Natural Language Processing NLP we design a time series foundation model for forecasting whose out of the

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We propose a self supervised method for pre training universal time series representations in which we learn contrastive representations using similarity distillation along the temporal and

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We propose Crossformer a Transformer based model that explicitly utilizes cross dimension dependency for multivariate time series forecasting

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Abstract Despite the success of self supervised pre training in texts and images applying it to multivariate time series MTS falls behind tailored methods for tasks like forecasting