MODEL PROFILE

ttm-research-r2

ibm-research · ttm-research-r2

TinyTimeMixers (TTMs) are compact pre-trained models for Multivariate Time-Series Forecasting, open-sourced by IBM Research. **With model sizes starting from 1M params, TTM (accepted in NeurIPS 24) introduces the notion of the first-ever “tiny” pre-trained models for Time-Series Forecasting.** This model card contains the model-weights for research-use only and full reproducibility of our results published in our paper. However - if you are looking for TTM model weights for commercial and enterprise use, please refer to our granite releases here TTM outperforms several popular benchmarks demanding billions of parameters in zero-shot and few-shot forecasting. TTMs are lightweight forecasters, pre-trained on publicly available time series data with various augmentations. TTM provides state-of-the-art zero-shot forecasts and can easily be fine-tuned for multi-variate forecasts with just 5% of the training data to be competitive. Refer to our paper for more details.

Tabular & time serieslicense:cc-by-nc-sa-4.0
Context windowNot disclosed
Input → Output待核验 → 待核验
Model weightsNot verified
Profile updated2026/11/2 19:16:40
OVERVIEW

Model overview

TinyTimeMixers (TTMs) are compact pre-trained models for Multivariate Time-Series Forecasting, open-sourced by IBM Research. **With model sizes starting from 1M params, TTM (accepted in NeurIPS 24) introduces the notion of the first-ever “tiny” pre-trained models for Time-Series Forecasting.** This model card contains the model-weights for research-use only and full reproducibility of our results published in our paper. However - if you are looking for TTM model weights for commercial and enterprise use, please refer to our granite releases here TTM outperforms several popular benchmarks demanding billions of parameters in zero-shot and few-shot forecasting. TTMs are lightweight forecasters, pre-trained on publicly available time series data with various augmentations. TTM provides state-of-the-art zero-shot forecasts and can easily be fine-tuned for multi-variate forecasts with just 5% of the training data to be competitive. Refer to our paper for more details.

Key features

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Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

许可证:cc-by-nc-sa-4.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。