TimesFM 2.5
Google's zero-shot time series forecasting model, now with 16k context
Expert verdict
Ship
3-1The Panel's Take
TimesFM 2.5 is the latest update to Google Research's pretrained time-series foundation model — a 200M parameter decoder-only model that does zero-shot forecasting across virtually any time-series domain without needing to retrain or fine-tune. Released March 31, 2026, it expands context length to 16,000 time steps (up from earlier versions) and adds an optional 30M continuous quantile head for probabilistic forecasting up to 1,000 steps ahead. Unlike traditional forecasting approaches that require training a new model per dataset, TimesFM was pre-trained on 100 billion real-world time points across diverse domains. You point it at new data — retail sales, server metrics, energy demand, financial prices — and it forecasts without any additional training. The March 31 update also restores covariate (XReg) support and updates inference APIs for better integration. With 14,000 GitHub stars and trending today, TimesFM is becoming the default baseline for time-series work in the same way BERT became the baseline for NLP tasks. Google Cloud users get it directly via BigQuery ML's AI.FORECAST function. For everyone else, it's available on HuggingFace and installable as a Python package.
Share this verdict
TimesFM 2.5 verdict: SHIP 🚀 3 ships · 1 skip from the expert panel Full review: shiporskip.io/tool/timesfm-25-google-time-series-foundation-model
Weekly AI Tool Verdicts
Get the next verdict in your inbox
7 critics review a new AI tool every day. Weekly digest — free.
Compare TimesFM 2.5 with Others
Looking for TimesFM 2.5 alternatives?
Compare TimesFM 2.5 with every other Data & Analytics tool reviewed by our panel.
See all Data & Analytics alternativesEmbed this verdict
Tool makers can add a live ShipOrSkip badge to their site. Badge loads track impressions; clicks route back to this review.
<a href="https://shiporskip.io/api/badge-click/timesfm-25-google-time-series-foundation-model" target="_blank" rel="noopener"><img src="https://shiporskip.io/api/badge/timesfm-25-google-time-series-foundation-model" alt="TimesFM 2.5 Ship verdict on ShipOrSkip" width="360" height="90" /></a>[](https://shiporskip.io/api/badge-click/timesfm-25-google-time-series-foundation-model)<iframe src="https://shiporskip.io/embed/timesfm-25-google-time-series-foundation-model" title="TimesFM 2.5 ShipOrSkip verdict" width="360" height="260" style="border:0;border-radius:16px;max-width:100%;" loading="lazy"></iframe>The reviews
“Zero-shot forecasting that competes with supervised models trained specifically on your dataset is remarkable. The BigQuery ML integration makes this accessible to data teams without ML infrastructure. 16k context is enough for 13+ years of daily data.”
“Zero-shot is impressive in benchmarks but enterprise forecasting often has domain-specific seasonality and causal structure that a foundation model can't infer without fine-tuning. The 200M parameter model still requires non-trivial GPU resources for self-hosting.”
“Time-series is the dark matter of AI applications — it's everywhere (supply chains, energy grids, healthcare) but historically required expensive specialist models. Foundation models democratizing this could unlock huge productivity in industries that have been stuck with Excel.”
“For content creators tracking engagement trends, ad performance, or audience growth, having a zero-shot model that can forecast without a data science team is genuinely empowering. Hook it up to your analytics data and stop guessing.”