@IndieDevHailey: Google quietly releases time series nuclear weapon TimesFM: Predict the future in 5 minutes! Sales forecasting, stock price trends, website traffic, energy load, cryptocurrency volatility... These headache-inducing future numbers now have a unified answer. TimesFM: → Trained on 100 billion real-world time series data...
Summary
Google has released TimesFM, a time series forecasting model trained on 100 billion real-world time series data, supporting zero-shot prediction. It is free, open-source, and can run locally on ordinary computers.
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Google quietly drops time series nuclear weapon TimesFM: Predict the future in 5 minutes!
Sales forecasts, stock trends,
website traffic, energy loads,
cryptocurrency volatility……
All those headache-inducing future numbers now have a single answer.
TimesFM:
→ Trained on 100 billion real-world time series data
→ Zero-shot direct prediction, no fine-tuning or training needed
→ Feed it a slice of historical data, get results in seconds, complete with confidence intervals
Completely free + fully open source, runs locally on an ordinary computer.
Late nights tuning ARIMA, Prophet, Excel — now it’s done with one line of code.
Google itself already uses it at scale in BigQuery and Sheets.
Prediction has finally become simple.
Developer Hailey (@IndieDevHailey):
Never sending data to Google again! 🔥
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Google has released TimesFM, an AI model for zero-shot time series forecasting, trained on 100 billion real data points, free and open-source.
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Google Research releases TimesFM 2.5, an open-source time-series foundation model for forecasting, with 200M parameters, 16k context length, and support for quantile forecasts up to 1k horizon. The model is available on PyPI and Hugging Face, with fine-tuning via LoRA and integration into Google products like BigQuery ML, Google Sheets, and Vertex Model Garden.
TimesFM-3: A zero-shot foundation model for multivariate forecasting
Google introduces TimesFM-3, a state-of-the-art zero-shot foundation model for multivariate time series forecasting, capable of handling multiple targets and covariates in a single forward pass without fine-tuning.
google/timesfm-3.0-pytorch
TimesFM 3.0 is a pretrained time-series foundation model by Google Research, released on Hugging Face with PyTorch weights for time-series forecasting tasks.