Mantis: Lightweight Foundation Model for Time Series Classification

🚨 Version 1.1.0 is released: more tutorials, better default prediction head, UTICA's checkpoint support!

😎 Mantis was published at ICML'26, see the paper!

Overview

Mantis is a family of open-source time series classification foundation models.

The key features of Mantis:

• Zero-shot feature extraction: The model can be used in a frozen state to extract deep features and train a classifier on them.
• Fine-tuning: To achieve the highest performance, the model can be further fine-tuned for a new task.
• Lightweight: Our models contain a few million parameters, allowing us to fine-tune them on a single GPU (even feasible on a CPU).
• Calibration: In our studies, we have shown that Mantis is the most calibrated foundation model for classification so far.
• Adaptable to large-scale datasets: For datasets with a large number of channels, we propose additional adapters that reduce memory requirements.

Below we give instructions on how the package can be installed and used.

Installation

Pip installation

It can be installed via pip by running:

pip install mantis-tsfm

The requirements can be verified at pyproject.toml.

Editable mode using uv

First, install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

To create the virtual environment and install the package in editable mode together with all dependencies, including the development ones, run:

uv sync

This uses the versions pinned in uv.lock. By default, uv picks a compatible Python interpreter, downloading one if needed. To choose the version yourself, run:

uv sync --python 3.10

If you want to run any command within the environment, instead of activating the environment manually, you can use uv run:

uv run <command>

For example, to run the tests:

uv run pytest

To update the pinned versions after changing the dependencies in pyproject.toml, run:

uv lock
uv sync

Getting started

Please refer to the getting_started/ folder to see reproducible examples of how the package can be used.

Below we summarize the basic commands needed to use the package.

Prepare Data.

As an input, Mantis accepts any time series whose sequence length is a multiple of 32, which corresponds to the number of tokens fixed in our model. We found that resizing time series via interpolation is generally a good choice:

import torch
import torch.nn.functional as F

def resize(X):
    X_scaled = F.interpolate(torch.tensor(X, dtype=torch.float), size=512, mode='linear', align_corners=False)
    return X_scaled.numpy()

Generally speaking, the interpolation size is a hyperparameter to play with. Nevertheless, since Mantis was pre-trained on sequences of length 512, interpolating to this length looks reasonable in most cases.

Initialization.

At the moment, we have two backbones and four checkpoints, including the UTICA checkpoint:

	Mantis	Mantis+	UTICA	MantisV2
Module	MantisV1	MantisV1	MantisV1	MantisV2
Checkpoint	paris-noah/Mantis-8M	paris-noah/MantisPlus	fegounna/Utica	paris-noah/MantisV2

To load any of these pre-trained models from Hugging Face, you can do as follows:

from mantis.architecture import MantisV1

network = MantisV1(device='cuda')
network = network.from_pretrained("paris-noah/Mantis-8M")

As we showed in our paper, the superior performance of the frozen encoder is achieved by using one of the intermediate representations together with the aggregated output-token strategy. For this, pass the return_transf_layer=layer_idx and output_token='combined' arguments when initializing the network. On UCR, the following intermediate layers give the best performance for each checkpoint (note that the count starts from 0):

	Mantis	Mantis+	UTICA	MantisV2
layer_idx	2	1	2	2

Please see getting_started/intermediate_layers.ipynb for more details.

The UTICA checkpoint is pre-trained with a self-distillation recipe and is hosted outside of the paris-noah collection, so we recommend pinning its revision:

network = MantisV1(device='cuda')
network = network.from_pretrained("fegounna/Utica", revision="3cff4f954191b5bf9839b7a41117e2b24e7693ab")

See getting_started/utica.ipynb for a complete example, including how return_transf_layer and output_token affect its accuracy.

Feature Extraction.

We provide a scikit-learn-like wrapper MantisTrainer that allows you to use Mantis as a feature extractor by running the following commands:

from mantis.trainer import MantisTrainer

model = MantisTrainer(device='cuda', network=network)
Z = model.transform(X) # X is your time series dataset

Once you have extracted the features, you can train any classifier you want. Note that feature normalization is important if you use a linear classifier:

Sklearn Log. Regression (L-BFGS-B Opt.)			PyTorch Linear (Adam Opt.)			Random Forest
W/o Norm	MinMax Scaler	Standard Scaler	W/o Norm	Layer Norm	Batch Norm
0.763	0.829	0.837	0.669	0.71	0.827	0.82

Our features can also be concatenated with those of another model. In particular, TiViT extracts time series features with a frozen Vision Transformer, and since it looks at the data from a completely different angle, its representations are complementary to ours: combining them improves the accuracy over either model alone. See getting_started/mantis_and_tivit.ipynb for a self-contained example.

Fine-tuning.

If you want to fine-tune the model on your supervised dataset, you can use the fit method of MantisTrainer:

from mantis.trainer import MantisTrainer

model = MantisTrainer(device='cuda', network=network)
model.fit(X, y) # y is a vector with class labels
probs = model.predict_proba(X)
y_pred = model.predict(X)

Since version 1.1.0, by default, the prediction head for fine-tuning is a batch normalization step + linear layer, as we found that it delivers superior performance:

Fine-tuning head	UCR-128 accuracy
Linear	84.48 ± 0.33%
LayerNorm + Linear	85.00 ± 0.01%
BatchNorm + Linear	85.69 ± 0.06%

Adapters.

We have integrated into the framework the possibility to pass the input to an adapter before sending it to the foundation model. This may be useful for time series data sets with a large number of channels. More specifically, a large number of channels may induce the curse of dimensionality or make fine-tuning of the model infeasible.

A straightforward way to overcome these issues is to use a dimension reduction approach like PCA:

from mantis.adapters import MultichannelProjector

adapter = MultichannelProjector(new_num_channels=5, base_projector='pca')
adapter.fit(X)
X_transformed = adapter.transform(X)

model = MantisTrainer(device='cuda', network=network)
Z = model.transform(X_transformed)

Another way is to add learnable layers before the foundation model and fine-tune them with the prediction head:

from mantis.adapters import LinearChannelCombiner

model = MantisTrainer(device='cuda', network=network)
adapter = LinearChannelCombiner(num_channels=X.shape[1], new_num_channels=5)
model.fit(X, y, adapter=adapter, fine_tuning_type='adapter_head')

Pre-training.

The model can be pre-trained using the pretrain method of MantisTrainer that supports data parallelization. You can see a pre-training demo at getting_started/pretrain.py. For example, to pre-train the model on 4 GPUs, you can run the following commands:

cd getting_started/
python -m torch.distributed.run --nproc_per_node=4 --nnodes=1 pretrain.py --seed 42

We have open-sourced CauKer 2M, the synthetic data set we used to pre-train the two versions of Mantis, resulting in MantisPlus and MantisV2 checkpoints. The pretrain method directly supports a HF dataset as an input.

Structure

├── data/                <-- two datasets for demonstration
├── getting_started/     <-- jupyter notebooks with tutorials
└── src/mantis/          <-- the main package
    ├── adapters/        <-- adapters for multichannel time series
    ├── architecture/    <-- foundation model architectures
    └── trainer/         <-- a scikit-learn-like wrapper for feature extraction or fine-tuning

License

This project is licensed under the Apache License 2.0. See the LICENSE file for more details.

Open-source Participation

We would be happy to receive feedback and integrate any suggestions, so do not hesitate to contribute to this project by raising a GitHub issue.

Citing Mantis 📚

If you use Mantis in your work, please cite our papers :)

1. The ICML paper that combines the contributions of V1 and V2:

@inproceedings{feofanov2026mantis,
title={Mantis: Lightweight Foundation Model for Time Series Classification},
author={Vasilii Feofanov and Songkang Wen and Shifeng Xie and Simon Roschmann and Marius Alonso and Hongbo Guo and Romain Ilbert and Malik Tiomoko and Quentin Bouniot and Zeynep Akata and Lujia Pan and Jianfeng Zhang and Ievgen Redko},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=gbJMAjXLZ4}
}

2. MantisV2 and Mantis+ report:

@article{feofanov2026mantisv2,
  title={Mantisv2: Closing the zero-shot gap in time series classification with synthetic data and test-time strategies},
  author={Feofanov, Vasilii and Wen, Songkang and Zhang, Jianfeng and Pan, Lujia and Redko, Ievgen},
  journal={arXiv preprint arXiv:2602.17868},
  year={2026}
}

3. Original tech report:

@article{feofanov2025mantis,
  title={Mantis: Lightweight Calibrated Foundation Model for User-Friendly Time Series Classification},
  author={Vasilii Feofanov and Songkang Wen and Marius Alonso and Romain Ilbert and Hongbo Guo and Malik Tiomoko and Lujia Pan and Jianfeng Zhang and Ievgen Redko},
  journal={arXiv preprint arXiv:2502.15637},
  year={2025},
}