·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Seattle Times and Newsday are the latest publications to sue OpenAI and Microsoft2h◆Hikers rescued after using Google Gemini for planning5h◆OpenAI confirms ‘wiki incident,’ says it’s ‘working on a framework’ for more disclosure7h◆OpenAI admits to German wiki ‘incident’14h◆XDOF, just three months out of stealth, is in talks for a Series B at a $1.2B valuation1d◆OpenAI’s rogue agents keep escaping, with no formal process to investigate them1d◆AI compute provider Nscale is looking for $3.5B in pre-IPO financing1d◆Architecting memory and storage in the AI era1d◆Roland is getting into generative AI music with Melody Flip1d◆What will Apple’s John Ternus era look like?1d◆Another swarm of OpenAI agents reached the open internet without the frontier lab’s knowledge1d◆Microsoft says virtually nobody was grabbing NYT articles through its chatbot1d◆Apple’s Ternus era begins as Nvidia bets on the whole AI stack1d◆Google’s Gemini Spark can now manage your Google Photos library1d◆Less than 24 hours to apply for your TechCrunch Disrupt 2026 Side Event1d◆Rogue OpenAI agents appear to have organized another attack using a German wiki1d◆Instagram’s AI detection is a mess (again)1d◆Why AI food looks like that1d◆Microsoft’s Project Zenith is a ‘distraction-free Windows experience’ for developers1d◆Sam Altman apologizes for ‘messy’ GPT-6 Astra rollout that’s locked out paying users1d◆Seattle Times and Newsday are the latest publications to sue OpenAI and Microsoft2h◆Hikers rescued after using Google Gemini for planning5h◆OpenAI confirms ‘wiki incident,’ says it’s ‘working on a framework’ for more disclosure7h◆OpenAI admits to German wiki ‘incident’14h◆XDOF, just three months out of stealth, is in talks for a Series B at a $1.2B valuation1d◆OpenAI’s rogue agents keep escaping, with no formal process to investigate them1d◆AI compute provider Nscale is looking for $3.5B in pre-IPO financing1d◆Architecting memory and storage in the AI era1d◆Roland is getting into generative AI music with Melody Flip1d◆What will Apple’s John Ternus era look like?1d◆Another swarm of OpenAI agents reached the open internet without the frontier lab’s knowledge1d◆Microsoft says virtually nobody was grabbing NYT articles through its chatbot1d◆Apple’s Ternus era begins as Nvidia bets on the whole AI stack1d◆Google’s Gemini Spark can now manage your Google Photos library1d◆Less than 24 hours to apply for your TechCrunch Disrupt 2026 Side Event1d◆Rogue OpenAI agents appear to have organized another attack using a German wiki1d◆Instagram’s AI detection is a mess (again)1d◆Why AI food looks like that1d◆Microsoft’s Project Zenith is a ‘distraction-free Windows experience’ for developers1d◆Sam Altman apologizes for ‘messy’ GPT-6 Astra rollout that’s locked out paying users1d◆
DataBubble·

Model Detail

google logo

timesfm-3.0-pytorch

▲ 124.7%
Provider: GoogleCategory: otherPipeline: time-series-forecasting
DB Score
43.7
Downloads
123K
Likes
453
Day
+124.7%
Week
+0.0%
Month
+0.0%
Overview

timesfm-3.0-pytorch is an AI model with 165M parameters released by Google. The model is registered under the time-series-forecasting pipeline tag on Hugging Face, distributed under a other license.

Technical

timesfm-3.0-pytorch ships with 165M parameters. Distribution is governed by the other license — review the exact terms before commercial deployment.

Trending Signal

Downloads of timesfm-3.0-pytorch have moved +124.7% over the past 24 hours. That is a slight downtrend, consistent with normal cooling as newer models compete for the same workloads. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.

Read about databubble_score →
Use Cases

timesfm-3.0-pytorch is best fit for workloads that match the time-series-forecasting pipeline tag. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.

Download History
Research Paper
arXiv: 2310.10688→
Model Info
Licenseother
Recent newsView all news →
Related News
arxivneutral49d ago

When Directional Accuracy Lies: A Base-Rate-Honest Benchmark for LoRA-Adapted TimesFM on Equity Forecasting

arXiv:2607.12248v2 Announce Type: replace-cross Abstract: Large pretrained time-series models such as TimesFM are attractive for financial forecasting, but raw directional accuracy is a misleading scoreboard in equity markets. An early LoRA adapter in this project appeared to reach roughly 80% direc

Related Models
google logo
gemma-4-31B-it
Google · 8.3M downloads
google logo
gemma-4-26B-A4B-it
Google · 8.3M downloads
openai logo
clip-vit-large-patch14
OpenAI · 33.1M downloads
openai logo
clip-vit-base-patch32
OpenAI · 20.8M downloads
HomeModelsNews