OTiS: An open model for general time series analysis

This is the official PyTorch implementation of our work Towards Generalisable Time Series Understanding Across Domains (2024).

Environment setup

Run the following commands from the root directory of this project to setup the environment and install the timm library. Note that this command block is only executed once during the initial environment setup.

conda env create --file envs/otis.yaml
conda activate otis
git clone https://github.com/oetu/pytorch-image-models.git
pip install -e pytorch-image-models

Activate the conda environment before running OTiS.

conda activate otis

Model weights

Download pre-trained model weights from the google drive.

Training

Classification

Run the following command.

python3 main_finetune.py --num_workers $num_workers --seed $sd --downstream_task classification --nb_classes $nb_classes --input_channels $input_channels --input_variates $input_variates --time_steps $time_steps --patch_height $patch_height --patch_width $patch_width --model $model --batch_size $bs --epochs $epochs --blr $lr --warmup_epochs $warmup_epochs --data_path $data_path --labels_path $labels_path --val_data_path $val_data_path --val_labels_path $val_labels_path --output_dir $output_dir

For slurm, run the following command.

torchrun --rdzv-endpoint=localhost:$port --nproc_per_node $world_size --nnodes $nodes --node_rank 0 main_finetune.py --world_size $world_size --dist_eval --num_workers $num_workers --seed $sd --downstream_task classification --nb_classes $nb_classes --input_channels $input_channels --input_variates $input_variates --time_steps $time_steps --patch_height $patch_height --patch_width $patch_width --model $model --batch_size $bs --blr $lr --epochs $epochs --warmup_epochs $warmup_epochs --data_path $data_path --labels_path $labels_path --val_data_path $val_data_path --val_labels_path $val_labels_path --output_dir $output_dir

Regression

Run the following command for a multi-output regression with N variables.

python3 main_finetune.py --num_workers $num_workers --seed $sd --downstream_task regression --nb_classes N --lower_bnd 0 --upper_bnd N --input_channels $input_channels --input_variates $input_variates --time_steps $time_steps --patch_height $patch_height --patch_width $patch_width --model $model --batch_size $bs --blr $lr --epochs $epochs --warmup_epochs $warmup_epochs --data_path $data_path --labels_path $labels_path --val_data_path $val_data_path --val_labels_path $val_labels_path --output_dir $output_dir

For slurm, run the following command.

torchrun --rdzv-endpoint=localhost:$port --nproc_per_node $world_size --nnodes $nodes --node_rank 0 main_finetune.py --world_size $world_size --dist_eval --num_workers $num_workers --seed $sd --downstream_task regression --nb_classes N --lower_bnd 0 --upper_bnd N --input_channels $input_channels --input_variates $input_variates --time_steps $time_steps --patch_height $patch_height --patch_width $patch_width --model $model --batch_size $bs --blr $lr --epochs $epochs --warmup_epochs $warmup_epochs --data_path $data_path --labels_path $labels_path --val_data_path $val_data_path --val_labels_path $val_labels_path --output_dir $output_dir

Forecasting

Run the following command.

python3 main_forecast.py --num_workers $num_workers --seed $sd --downstream_task forecasting --mask_ratio $mr --input_channels $input_channels --input_variate$input_variates --time_steps $time_steps --patch_height $patch_height --patch_width $patch_width --ncc_weight $ncc --model $model --batch_size $bs --blr $blr --epochs $epochs --warmup_epochs $warmup_epochs --data_path $data_path --val_data_path $val_data_path --output_dir $output_dir

For slurm, run the following command.

torchrun --rdzv-endpoint=localhost:$port --nproc_per_node $world_size --nnodes $nodes --node_rank 0 main_forecast.py --num_workers $num_workers --seed $sd --downstream_task forecasting --mask_ratio $mr --input_channels $input_channels --input_variates $input_variates --time_steps $time_steps --patch_height $patch_height --patch_width $patch_width --ncc_weight $ncc --model $model --batch_size $bs --blr $blr --epochs $epochs --warmup_epochs $warmup_epochs --data_path $data_path --val_data_path $val_data_path --output_dir $output_dir

Evaluation

Use the --eval flag. For classification tasks, e.g. run the following command.

python3 main_finetune.py --eval --resume $checkpoint --num_workers $num_workers --seed $sd --downstream_task classification --nb_classes $nb_classes --input_channels $input_channels --input_variates $input_variates --time_steps $time_steps --patch_height $patch_height --patch_width $patch_width --model $model --batch_size $batch_size --epochs $epochs --blr $blr --warmup_epoch $warmup_epochs --data_path $data_path --labels_path $labels_path --val_data_path $val_data_path --val_labels_path $val_labels_path --output_dir $output_dir

Results

Quantitative

Qualitative

Citation

Please cite the following work:

@article{turgut2024towards,
  title={Towards Generalisable Time Series Understanding Across Domains},
  author={Turgut, {\"O}zg{\"u}n and M{\"u}ller, Philip and Menten, Martin J and Rueckert, Daniel},
  journal={arXiv preprint arXiv:2410.07299},
  year={2024}
}

Notice

This project includes third-party software components that are subject to their respective licenses. Detailed information including component names, licenses, and copyright holders is provided in the respective files. Please review the LICENSE file before using or distributing this software.