<ul><li>Transformer-based models like Chronos and PatchTST are revolutionizing time-series forecasting, offering unparalleled accuracy and adaptability for complex datasets.</li><li>Transformers, originally developed for natural language processing, excel in capturing long-range dependencies in data, providing greater speed and accuracy in forecasting various industries.</li><li>Chronos, utilizing self-attention mechanisms, excels in understanding intricate temporal relationships and is scalable for diverse forecasting tasks across domains like stock market analysis and energy demand forecasting.</li><li>PatchTST focuses on segmenting data into smaller patches, allowing for localized pattern detection, making it ideal for irregular or noisy datasets in industries like healthcare and environmental monitoring.</li></ul>

Forecast Anything with Transformers with Chronos or PatchTST

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