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Snapshot Window: 2026-09-06 09:10 UTC ยท โ† Back to Crypto Overview

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Total Engagement - Comments: 0, Retweets: 0, Likes: 0, Impressions: 5

Verbatim Community Citations & Social Evidence 1 source posts analyzed

GOOGLE HAS SILENTLY RELEASED AN AI MODEL THAT PREDICTS PATTERNS It's called TimesFM โ†’ Predicts sales, demand, market prices, web traffic, crypto volatility... โ†’ Trained on 100 billion real data points โ†’ Works in zero-shot, no need to train it โ†’ Runs 100% locally, total

The image shows a GitHub README page for "TimesFM" (Time Series Foundation Model), developed by Google Research for time-series forecasting, released under an Apache-2.0 license. The page references the ICML 2024 paper "A decoder-only foundation model for time-series forecasting" and highlights TimesFM 3.0, with checkpoints available via the "TimesFM Hugging Face Collection" and a forthcoming Google Research blog post. It also lists Google's 1P product integrations (BigQuery ML, Google Sheets, and Vertex Model Garden) and notes archived versions 2.5, 2.0, and 1.0. This directly supports the accompanying post's claim about Google's release of TimesFM, a versatile AI model for predicting patterns in sales, demand, market prices, web traffic, and crypto volatility, trained on massive real-world data and capable of zero-shot forecasting.

AI visual note: The image shows a GitHub README page for "TimesFM" (Time Series Foundation Model), developed by Google Research for time-series forecasting, released under an Apache-2.0 license. The page references the ICML 2024 paper "A decoder-only foundation model for time-series forecasting" and highlights TimesFM 3.0, with checkpoints available via the "TimesFM Hugging Face Collection" and a forthcoming Google Research blog post. It also lists Google's 1P product integrations (BigQuery ML, Google Sheets, and Vertex Model Garden) and notes archived versions 2.5, 2.0, and 1.0. This directly supports the accompanying post's claim about Google's release of TimesFM, a versatile AI model for predicting patterns in sales, demand, market prices, web traffic, and crypto volatility, trained on massive real-world data and capable of zero-shot forecasting.

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