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www.eliza-ng.me | ||
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ssc.io
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| | | | | Domain generalization aims to design models that can effectively generalize to unseen target domains by learning from observed source domains. Domain generalization poses a significant challenge for time series data, due to varying data distributions and temporal dependencies. Existing approaches to domain generalization are not designed for time series data, which often results in suboptimal or unstable performance when confronted with diverse temporal patterns and complex data characteristics. We propose a novel approach to tackle the problem of domain generalization in time series forecasting. We focus on a scenario where time series domains share certain common attributes and exhibit no abrupt distribution shifts. Our method revolves around the incorpora... | |
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anitagraser.com
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| | | | | tldr; Maybe. Preliminary results certainly are impressive. Introduction Crowd and flow predictions have been very popular topics in mobility data science. Traditional forecasting methods rely on classic machine learning models like ARIMA, later followed by deep learning approaches such as ST-ResNet. More recently, foundation models for timeseries forecasting, such as TimeGPT, Chronos, and LagLlama have... | |
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andlukyane.com
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| | | | | My review of the paper Chronos Learning the Language of Time Series | |
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blog.ml.cmu.edu
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| | | The latest news and publications regarding machine learning, artificial intelligence or related, brought to you by the Machine Learning Blog, a spinoff of the Machine Learning Department at Carnegie Mellon University. | ||
