A plain-English guide to Google's TimesFM: what it is, how it's built, why it's called a "foundation model", and what you can do with it, from forecasting COâ‚‚ and oil prices to World Series odds, running free in a web browser.
Try it now: https://vishalmysore.github.io/webTimesFM/
Code: https://github.com/vishalmysore/webTimesFM
Model files (ONNX): https://huggingface.co/VishalMysore/timesfm-2.5-200m-onnx-web
What is TimesFM?
TimesFM (short for Time Series Foundation Model) is an open AI model from Google Research that forecasts numbers over time. Give it any list of numbers in time order, such as daily sales, monthly rainfall, hourly electricity use or a stock price, and it predicts what comes next, together with a range of likely outcomes.
What makes it special is that you don't have to train it. TimesFM has already learned what trends, seasons and cycles look like from about 100 billion real-world data points. It can forecast a series it has never seen before, straight away. This is called zero-shot forecasting.
It was introduced in the research paper "A decoder-only foundation model for time-series forecasting" by Abhimanyu Das, Weihao Kong, Rajat Sen and Yichen Zhou (ICML 2024). The code and the model weights up to version 2.5 are open source under the Apache-2.0 licence.
In one sentence: TimesFM is like ChatGPT, but instead of reading and writing words, it reads and writes numbers.
What is a time-series foundation model?
A foundation model is a large AI model that is:
- Pre-trained once on a huge and varied dataset,
- General-purpose, not built for one task,
- Usable straight away on new problems (zero-shot), and
- Adaptable: it can be fine-tuned further if you want.
GPT, Gemini and Claude are foundation models for text. TimesFM is a foundation model for time series, along with others such as Amazon's Chronos, Salesforce's Moirai, Lag-Llama and Nixtla's TimeGPT.
TimesFM vs traditional forecasting (ARIMA, Prophet)
| Traditional model (ARIMA, Prophet, ETS) | Foundation model (TimesFM) | |
|---|---|---|
| Training | Fitted separately for each series | Trained once, used for everything |
| Setup | Needs a data scientist to choose and tune | Paste your numbers, get a forecast |
| Knowledge | Knows only that one series | Has "seen" millions of patterns |
| Uncertainty | Often a single line, or a fitted interval | Built-in quantiles (10%…90%) for every step |
| Best when | You have one important series and time to tune | You have many series, little time, or little history |
How TimesFM is built
The core idea: GPT's architecture, applied to numbers
TimesFM is a decoder-only transformer, the same family of neural network as GPT, Gemini and Llama, from Google's 2017 paper "Attention Is All You Need". Google's key change is what counts as a "token":
| Language model (GPT-style) | TimesFM | |
|---|---|---|
| Input token | a word or piece of a word | a patch of 32 consecutive numbers |
| Task | predict the next word | predict the next 128 numbers |
| Trained on | text from the internet | ~100 billion real time points |
| Output | probabilities over a vocabulary | actual values plus quantiles (a range) |
Feeding the model patches instead of single values (an idea also used by PatchTST) makes long histories cheap to read. Predicting a long output patch of 128 steps at once makes forecasts fast and stable.
Inside TimesFM 2.5
These values come from the model's published configuration:
Your numbers ──► split into patches of 32 ──► normalise each patch (mean and spread)
│
â–¼
Input block: a small residual neural network turns each patch into a 1,280-number token
│
â–¼
20 transformer layers × 16 attention heads
• each patch attends only to earlier patches (decoder-only, causal)
• rotary position embeddings (knows the order)
• RMS normalisation and QK-norm (stable training)
│
â–¼
Output block ──► next 128 steps × [mean, 10th, 20th … 90th percentile]
| Setting | TimesFM 2.5 |
|---|---|
| Parameters | about 200 million |
| Transformer layers | 20 |
| Model width | 1,280, with 16 attention heads |
| Input patch | 32 time steps |
| Output per pass | 128 time steps |
| Longest history (context) | 16,384 points |
| Uncertainty | 9 quantiles per step, plus an optional quantile head for horizons up to 1,024 |
For horizons longer than 128 steps, the model feeds its own forecast back in and goes again, the way a chatbot writes one word after another.
What TimesFM was trained on
According to the original paper, the pre-training mix includes:
- Google Trends: search interest over time, at hourly, daily, weekly and monthly scales.
- Wikipedia page views: daily traffic for millions of articles.
- Synthetic data: computer-generated series with known trends, seasons and patterns.
- Public datasets: electricity, traffic, weather, retail and economic series.
That variety is why one model can forecast carbon dioxide, sunspots, shop sales and oil prices.
The technology stack
- Software: Google trained TimesFM with JAX (the first version used Google's PaxML framework). For 2.5, Google publishes both a Flax (JAX) and a PyTorch implementation.
- Hardware: Google's TPUs (Tensor Processing Units), its custom AI chips.
-
Weights: shared on Hugging Face as a
safetensorsfile (925 MB for 2.5).
TimesFM versions: 1.0, 2.0, 2.5 and 3.0
| Version | Released | Size | Max history | What changed | Weights licence |
|---|---|---|---|---|---|
| TimesFM 1.0 | 2024 | 200M | 512 | The original, from the ICML paper | Apache-2.0 |
| TimesFM 2.0 | 2024–25 | 500M | 2,048 | Bigger model, more data | Apache-2.0 |
| TimesFM 2.5 | Sept 2025 | 200M | 16,384 | Smaller but longer memory; continuous quantile head; no frequency setting; covariates via XReg (Oct 2025) | Apache-2.0 |
| TimesFM 3.0 | Aug 2026 | larger | long | Native multivariate forecasting and covariates; tops fev-bench, the TIME benchmark and GIFT-Eval (per Google) | Non-commercial for downloaded weights; commercial use through Google Cloud BigQuery ML |
Why this project uses TimesFM 2.5: it's small enough to download into a browser, it can read up to 16k points of history, and its Apache-2.0 weights can be used freely, including commercially.
What we built: TimesFM in your web browser
webTimesFM runs Google's TimesFM 2.5 entirely inside a web page. There's no server, no Python and no sign-up, and your data never leaves your computer.
Google's PyTorch weights (safetensors, 925 MB)
└─► converted to ONNX (portable model format)
└─► quantised to 8-bit (261 MB) or 4-bit (146 MB)
└─► ONNX Runtime Web (runs AI models in the browser)
└─► WebGPU (your graphics card) or WASM (your CPU)
└─► timesfm.js + index.html on GitHub Pages
- Speed: a 128-step forecast takes about 50–300 milliseconds on a laptop GPU.
- Two model sizes: 8-bit gives practically the same answers as Google's original. 4-bit is a smaller download but can drift on short series.
- Proof it's the real model: a Run self-test vs PyTorch button compares the browser's forecasts with forecasts made by Google's own Python code, on your machine.
What TimesFM does well, and where it fails
Every example on the site has a Hold back option. It hides the most recent data, forecasts it, and scores the result against the simplest possible guess, "no change".
Where it shines: regular patterns
COâ‚‚ at Mauna Loa: a steady rise plus a yearly cycle. TimesFM forecast five years ahead almost exactly, 95% better than "no change".
Sunspots: the ~11-year solar cycle. Reading 85 years of history, TimesFM predicted the next cycle's rise and peak a decade ahead.
Where it struggles: shocks from outside the data
World extreme poverty: TimesFM saw the steady decline up to 2018 and expected it to continue. It couldn't know COVID-19 would stall progress in 2020.
The rule of thumb: TimesFM is excellent at patterns (trends, seasons, cycles), but it can't see events (pandemics, wars, policy changes) until they show up in the numbers. For prices, "no change" is very hard to beat; the uncertainty band is the most useful output.
Forecasting current affairs: World Series, oil and Bitcoin
The Current affairs tab points TimesFM at this week's news, with data refreshed automatically every day by a GitHub Action.
World Series odds combine three steps:
- Elo strength ratings from every MLB game this season,
- TimesFM forecasting each team's strength with an uncertainty band,
- 100,000 simulated playoffs in the browser, starting from the real series score.
On 17 October 2026, after the Brewers swept the Dodgers, the model gives Milwaukee about a 71% chance of winning the World Series. TimesFM's band moves the odds only slightly. The series score and the season's results do most of the work, and the page says so.
Oil and Bitcoin get 30-day forecasts with uncertainty bands, and the page explains why prices are so hard to forecast.
Adding the news: sentiment as a warning light
TimesFM reads only numbers, so a second, much smaller model reads the headlines. Every day, up to 100 Google News headlines per topic (oil, Bitcoin, each playoff team) are scored with VADER, a lightweight sentiment model. They're averaged into a daily news tone from −10 to +10.
Tone isn't direction. "War escalates" sounds negative but pushes oil prices up. So the news never moves the forecast. It works as a warning light instead:
- âš Alert: fires when the latest day's tone is more than twice its usual day-to-day swing.
- Stormy-news band: the forecast chart then adds a purple range, built from how far that price moved in its most turbulent past periods.
Replaying August to October 2026 with no hindsight, three of five oil-news alerts came just before big Brent moves (+7%, +10%, +18%), and one big jump was missed. Even the stormy band was too narrow for the war spike. News can tell you when to worry, not always how much.
How to use TimesFM in your own web page
Copy timesfm.js next to your page, then:
<script type="module">
import * as ort from "https://cdn.jsdelivr.net/npm/onnxruntime-web@1.29.0/dist/ort.webgpu.min.mjs";
import { TimesFM } from "./timesfm.js";
ort.env.wasm.wasmPaths = "https://cdn.jsdelivr.net/npm/onnxruntime-web@1.29.0/dist/";
const model = await TimesFM.load(ort,
"https://huggingface.co/VishalMysore/timesfm-2.5-200m-onnx-web/resolve/main/timesfm25_q8.onnx",
navigator.gpu ? "webgpu" : "wasm");
const history = [/* your numbers, oldest first */];
const { point, quantiles } = await model.forecast(history, 128);
// point: median forecast; quantiles[t] = [mean, q10, q20, ..., q90]
</script>
No code? Open the site's Tutorial tab. It shows how to paste a column from Excel or Google Sheets, upload a CSV, choose context and horizon, and check accuracy with Hold back.
For Python, use Google's official package and examples at github.com/google-research/timesfm.
Try it
https://vishalmysore.github.io/webTimesFM/: open it, click Load model (a one-time 261 MB download), pick an example, and press Forecast. Everything runs on your own machine.
Disclaimer
Please read before using the forecasts on this site or in this article.
Not affiliated with Google
- TimesFM is created by Google Research: paper, code, TimesFM 2.5 weights. "Google" and "TimesFM" are names and trademarks of Google LLC.
- webTimesFM is an independent, unofficial community project. It is not made, sponsored, reviewed or endorsed by Google, and nothing here speaks for Google.
About the browser model
- The model running here is a conversion of Google's TimesFM 2.5 weights to ONNX, compressed to 8-bit and 4-bit. The weights' values come only from Google's release; they were not retrained or fine-tuned.
- Compression and the browser re-implementation of the pre- and post-processing mean its forecasts can differ slightly from Google's official code. The 4-bit model differs more, especially on short series. Use the Run self-test vs PyTorch button to check on your own machine.
- TimesFM 2.5 code and weights are licensed under Apache-2.0 and are provided "as is", without warranties or conditions of any kind. The changes made for this conversion are listed on the model card. TimesFM 3.0, which has a separate non-commercial licence, is not used or included.
Not financial, investment or betting advice
- The oil, Bitcoin and other price forecasts are educational demonstrations, not recommendations to buy, sell or hold anything. Prices behave much like a random walk, and in our own tests the model roughly ties a simple "no change" guess.
- The World Series odds are an illustration of a forecasting method, not betting tips. They ignore starting pitchers, injuries and much else that matters.
- Forecasts can be, and often are, wrong. Past accuracy doesn't guarantee future results. Don't make financial, betting, business, medical or policy decisions based on this site. Consult a qualified professional instead.
About the data and the news signal
- Data comes from third-party public sources: MLB Stats API, FRED (Federal Reserve Bank of St. Louis), IMF, World Bank, US Bureau of Labor Statistics, NOAA, SILSO / Royal Observatory of Belgium, Johns Hopkins CSSE and Google News. Each is subject to its own terms and is used here for non-commercial, educational purposes.
- Data can be delayed, revised or incomplete. For example, official oil prices lag the market by about a week. It's refreshed automatically once a day, with no manual checking, and accuracy isn't guaranteed.
- News headlines belong to their publishers and are linked, not copied in full. The news tone is scored automatically by a simple sentiment model (VADER). It can misread sarcasm, context or mixed stories, and it isn't an editorial judgement about any team, company or event.
- Team and league names are trademarks of their respective owners. This project isn't affiliated with or endorsed by Major League Baseball or any team.
Privacy
- Forecasts run entirely in your browser. Numbers you paste or upload aren't sent to any server by this site.
- Your browser does download files from third parties: the model from Hugging Face, and ONNX Runtime from the jsDelivr CDN. Those services may log the request, as any website does.
Use at your own risk
This is a free, open-source demonstration for learning and research. It's provided as is, with no warranty of any kind. The author isn't liable for any loss or damage arising from its use. Always verify important numbers with the original sources.
