TimesFM is a pretrained model developed by Google Research for forecasting time series data. Inspired by the success of large language models in natural language processing, TimesFM aims to provide accurate forecasts without requiring task-specific training. Key architectural components of TimesFM include patching, where the input time series is divided into smaller segments for efficient processing, a decoder-only model for predicting future patches based on previous ones, the ability to predict longer sequences of future values, and patch masking during training to improve adaptability. This novel deep learning model is designed to make forecasting tasks easier and more accurate, similar to how large language models can generate text without fine-tuning.
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Source link: https://medium.com/@ManueleCaddeo/time-series-forecasting-with-timesfm-2bfd8d09412d?source=rss——llm-5
TimesFM presents Time Series Forecasting by Manuele Caddeo #forecasting
