timesfm vs kronos
Two of the top projects in Open models, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
timesfm
Pretrained time-series foundation model for zero-shot forecasting from Google Research
kronos
Pretrained decoder-only models that forecast financial candlestick (K-line) series
Verdict
timesfm ranks higher, 65 to 47. Of the 4 score parts we measure for both, timesfm leads on 3, kronos on 1.
- Adoptionkronos leads by 32 points, 81 to 49: a wide gap.
- Freshnesstimesfm leads by 35 points, 100 to 65: a wide gap.
- Maintenancetimesfm leads by 48 points, 58 to 10: a wide gap.
- Agent-readytimesfm leads by 30 points, 30 to 0: a wide gap.
Pick timesfm if you are
Engineers and researchers forecasting time series without per-task training
- Zero-shot forecasting of univariate and multivariate series with past-only and future covariates
- Returns point forecast plus 9 quantiles (0.1 to 0.9) per step
Pick kronos if you are
Quant researchers and ML engineers forecasting candlestick time series
- Open weights at 4.1M, 24.7M and 102.3M params on Hugging Face
- Few-line predict and predict_batch API returning pandas DataFrames
| What we compare | timesfm | kronos |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 49, known | 81, widely used |
| Freshness | 100, active | 65, recent |
| Maintenance | 58, fair | 10, weak |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 30, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 34.5k | 40.8k |
| License | Apache-2.0 (permissive) | MIT (permissive) |
| Last commit | Sep 2026 | Apr 2026 |
| Last release | Aug 2026 | None published |
| Language | Python | Python |
| Docker | No | No |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Not stated |
timesfm
TimesFM is a pretrained decoder-only model for time-series forecasting, installed as a Python package with PyTorch or MLX (Apple silicon) backends. Version 3.0 forecasts univariate and multivariate series with past-only and past-and-future covariates, returning point forecasts plus nine quantiles (0.1 to 0.9). The code is Apache-2.0, but 3.0 weights carry a non-commercial license.
Who it is for: Engineers and researchers forecasting time series without per-task training
Strengths
- Zero-shot forecasting of univariate and multivariate series with past-only and future covariates
- Returns point forecast plus 9 quantiles (0.1 to 0.9) per step
- MLX backend runs 3.0 on Apple silicon without PyTorch, numerically matched to PyTorch
- Includes LoRA fine-tuning example (HF Transformers + PEFT), unit tests and an agent SKILL.md
Weaknesses
- TimesFM 3.0 weights are non-commercial and non-production; commercial use only via Google Cloud services
- Not an officially supported Google product
- Forecasting only; contexts beyond 15,360 points are truncated to the most recent
- README code imports differ between 3.0 examples and the PyPI 2.0.2 note, which is unclear
- GPU optional
- Needs PyTorch or MLX, Hugging Face checkpoints
- Models: TimesFM 3.0 (PyTorch checkpoint), TimesFM 2.5 (200M params, 16k context), TimesFM 1.0 and 2.0 (v1 archive)
kronos
Kronos is a family of decoder-only Transformers pretrained on OHLCV candlestick data from over 45 exchanges. A tokenizer quantizes K-lines into hierarchical discrete tokens, and the model forecasts future bars through a KronosPredictor class with temperature and top-p sampling. Weights for mini, small and base are on Hugging Face, and the repo includes a Qlib-based fine-tuning and backtest pipeline.
Who it is for: Quant researchers and ML engineers forecasting candlestick time series
Strengths
- Open weights at 4.1M, 24.7M and 102.3M params on Hugging Face
- Few-line predict and predict_batch API returning pandas DataFrames
- Includes tokenizer and predictor fine-tuning scripts with a Qlib backtest example
- MIT license; paper accepted at AAAI 2026
Weaknesses
- Kronos-large (499.2M) weights are not released
- Context limited to 512 tokens, except 2048 for Kronos-mini
- Batch prediction requires identical lookback and pred_len across all series
- Fine-tuning example is a demo, not a production trading system; no releases tagged
- GPU optional
- Needs PyTorch, Hugging Face Hub, pyqlib (fine-tuning only), Comet.ml (optional)
- Models: Kronos-mini, Kronos-small, Kronos-base, Hugging Face
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