TimesFM is a pretrained time-series foundation model developed by Google Research for time-series forecasting. It is a decoder-only foundation model for time-series forecasting.It is pre-trained on a large time-series corpus of 100 billion real world time-points, that displays impressive zero-shot performance on a variety of public benchmarks from different domains and granularities.
Helps with learning basic temporal patterns. Created using statistical models or physical simulations, it teaches the model the grammar of time series forecasting.
Adds depth and context. A large corpus of 100 billion time-points is curated from public time series datasets, including Google Trends and Wikipedia Pageviews. These datasets reflect real-world trends and patterns, enhancing the model's ability to generalize and understand domain-specific contexts not seen during training.
Google trains a decoder-only foundation model for time-series forecasting using a large pretraining corpus of 100 billion real-world time-points, primarily composed of search interest time-series data from Google Trends and pageviews from Wikipedia. They demonstrate that even a relatively small 200 million parameter pretrained model utilizing the TimesFM architecture exhibits impressive zero-shot performance across various public benchmarks from different domains and granularities.