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TinyTimeMixer (TTM) Model Card

TinyTimeMixers (TTMs) are compact pre-trained models for Multivariate Time-Series Forecasting, open-sourced by IBM Research. With less than 1 Million parameters, TTM introduces the notion of the first-ever “tiny” pre-trained models for Time-Series Forecasting.

TTM outperforms several popular benchmarks demanding billions of parameters in zero-shot and few-shot forecasting. TTM is pre-trained on diverse public time-series datasets which can be easily fine-tuned on your multi-variate target data. Refer to our paper for more details.

The current open-source version supports point forecasting use-cases ranging from minutely to hourly resolutions (Ex. 10 min, 15 min, 1 hour, etc.)

Note that zeroshot, fine-tuning and inference tasks using TTM can easily be executed in 1 GPU machine or in laptops too!!

Benchmark Highlights:

  • TTM (with less than 1 Million parameters) outperforms the following popular Pre-trained SOTAs demanding several hundred Million to Billions of parameters:
    • GPT4TS (NeurIPS 23) by 7-12% in few-shot forecasting.
    • LLMTime (NeurIPS 23) by 24% in zero-shot forecasting.
    • SimMTM (NeurIPS 23) by 17% in few-shot forecasting.
    • Time-LLM (ICLR 24) by 2-8% in few-shot forecasting
    • UniTime (WWW 24) by 27% in zero-shot forecasting.
  • Zero-shot results of TTM surpass the few-shot results of many popular SOTA approaches including PatchTST (ICLR 23), PatchTSMixer (KDD 23), TimesNet (ICLR 23), DLinear (AAAI 23) and FEDFormer (ICML 22).
  • TTM (1024-96, released in this model card with 1M parameters) outperforms pre-trained MOIRAI-Small (14M parameters) by 10%, MOIRAI-Base (91M parameters) by 2% and MOIRAI-Large (311M parameters) by 3% on zero-shot forecasting (horizon = 96). [notebook]
  • TTM quick fine-tuning also outperforms the competitive statistical baselines (Statistical ensemble and S-Naive) in M4-hourly dataset which existing pretrained TS models are finding difficult to outperform. [notebook]
  • TTM takes only a few seconds for zeroshot/inference and a few minutes for finetuning in 1 GPU machine, as opposed to long timing-requirements and heavy computing infra needs of other existing pre-trained models.

Model Description

TTM falls under the category of “focused pre-trained models”, wherein each pre-trained TTM is tailored for a particular forecasting setting (governed by the context length and forecast length). Instead of building one massive model supporting all forecasting settings, we opt for the approach of constructing smaller pre-trained models, each focusing on a specific forecasting setting, thereby yielding more accurate results. Furthermore, this approach ensures that our models remain extremely small and exceptionally fast, facilitating easy deployment without demanding a ton of resources.

Hence, in this model card, we plan to release several pre-trained TTMs that can cater to many common forecasting settings in practice. Additionally, we have released our source code along with our pretraining scripts that users can utilize to pretrain models on their own. Pretraining TTMs is very easy and fast, taking only 3-6 hours using 6 A100 GPUs, as opposed to several days or weeks in traditional approaches.

Each pre-trained model will be released in a different branch name in this model card. Kindly access the required model using our getting started notebook mentioning the branch name.

Model Releases (along with the branch name where the models are stored):

  • 512-96: Given the last 512 time-points (i.e. context length), this model can forecast up to next 96 time-points (i.e. forecast length) in future. Recommended for hourly and minutely resolutions (Ex. 10 min, 15 min, 1 hour, etc) (branch name: main)

  • 1024-96: Given the last 1024 time-points (i.e. context length), this model can forecast up to next 96 time-points (i.e. forecast length) in future. Recommended for hourly and minutely resolutions (Ex. 10 min, 15 min, 1 hour, etc) (branch name: 1024-96-v1)

  • Stay tuned for more models !

Model Details

For more details on TTM architecture and benchmarks, refer to our paper.

TTM-1 currently supports 2 modes:

  • Zeroshot forecasting: Directly apply the pre-trained model on your target data to get an initial forecast (with no training).

  • Finetuned forecasting: Finetune the pre-trained model with a subset of your target data to further improve the forecast.

Since, TTM models are extremely small and fast, it is practically very easy to finetune the model with your available target data in few minutes to get more accurate forecasts.

The current release supports multivariate forecasting via both channel independence and channel-mixing approaches. Decoder Channel-Mixing can be enabled during fine-tuning for capturing strong channel-correlation patterns across time-series variates, a critical capability lacking in existing counterparts.

In addition, TTM also supports exogenous infusion and categorical data which is not released as part of this version. Stay tuned for these extended features.

Recommended Use

  1. Users have to externally standard scale their data indepedently for every channel before feeding it to the model (Refer to TSP, our data processing utility for data scaling.)
  2. Enabling any upsampling or prepending zeros to virtually increase the context length for shorter length datasets is not recommended and will impact the model performance.

Model Sources

Uses 

# Load Model from HF Model Hub mentioning the branch name in revision field

model = TinyTimeMixerForPrediction.from_pretrained(
                "https://huggingface.co/ibm/TTM", revision="main"
            ) 

# Do zeroshot
zeroshot_trainer = Trainer(
        model=model,
        args=zeroshot_forecast_args,
        )
    )

zeroshot_output = zeroshot_trainer.evaluate(dset_test)


# Freeze backbone and enable few-shot or finetuning:

# freeze backbone
for param in model.backbone.parameters():
  param.requires_grad = False

finetune_forecast_trainer = Trainer(
        model=model,
        args=finetune_forecast_args,
        train_dataset=dset_train,
        eval_dataset=dset_val,
        callbacks=[early_stopping_callback, tracking_callback],
        optimizers=(optimizer, scheduler),
    )
finetune_forecast_trainer.train()
fewshot_output = finetune_forecast_trainer.evaluate(dset_test)

How to Get Started with the Model

Training Data

The TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:

Citation [optional]

Kindly cite the following paper, if you intend to use our model or its associated architectures/approaches in your work

BibTeX:

@article{ekambaram2024ttms,
  title={TTMs: Fast Multi-level Tiny Time Mixers for Improved Zero-shot and Few-shot Forecasting of Multivariate Time Series},
  author={Ekambaram, Vijay and Jati, Arindam and Nguyen, Nam H and Dayama, Pankaj and Reddy, Chandra and Gifford, Wesley M and Kalagnanam, Jayant},
  journal={arXiv preprint arXiv:2401.03955},
  year={2024}
}

APA:

Ekambaram, V., Jati, A., Nguyen, N. H., Dayama, P., Reddy, C., Gifford, W. M., & Kalagnanam, J. (2024). TTMs: Fast Multi-level Tiny Time Mixers for Improved Zero-shot and Few-shot Forecasting of Multivariate Time Series. arXiv preprint arXiv:2401.03955.

Model Card Authors

Vijay Ekambaram, Arindam Jati, Pankaj Dayama, Nam H. Nguyen, Wesley Gifford and Jayant Kalagnanam

IBM Public Repository Disclosure:

All content in this repository including code has been provided by IBM under the associated open source software license and IBM is under no obligation to provide enhancements, updates, or support. IBM developers produced this code as an open source project (not as an IBM product), and IBM makes no assertions as to the level of quality nor security, and will not be maintaining this code going forward.