Neural Jump ODEs

This repository is the official implementation of the papers

which are part of the series of works on Neural Jump ODEs that started with Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering.

The code is based on the code of the first paper, but was developed further such that it is more user-friendly. All experiments from the first paper can be run with this repo as well (see Instructions for Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering).

The experiments from the follow-up papers can be run with:

Instructions for Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
Instructions for Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework
Instructions for Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs
Instructions for Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs
Instructions for Running Experiments of Generative NJODE

A notebook studying Explainability in the context of NJODEs is available at Explainability of NJODEs.

A notebook comparing different coefficient estimation approaches using the NJODE for a generative model is available at Generative NJODE notebook.

Requirements

This code was executed using Python 3.7.

To install requirements, download this Repo and cd into it.

Then create a new environment and install all dependencies and this repo. With conda:

conda create --name njode python=3.7
conda activate njode
pip install -r requirements.txt

To use the Telegram-Bot see installation instructions here. The code will run without the Telegram-Bot, but you will not receive notification and results via Telegram when the training is finished (useful when running on a server).

Usage, License & Citation

This code can be used in accordance with the LICENSE.

If you find this code useful or include parts of it in your own work, please cite our papers:

Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs

@article{PDNJODE
  url = {https://arxiv.org/abs/2206.14284},
  author = {Krach, Florian and Nübel, Marc and Teichmann, Josef},
  title = {Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs},
  publisher = {arXiv},
  year = {2022},
}

Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework

@article{
    andersson2024extending,
    title={Extending Path-Dependent {NJ}-{ODE}s to Noisy Observations and a Dependent Observation Framework},
    author={William Andersson and Jakob Heiss and Florian Krach and Josef Teichmann},
    journal={Transactions on Machine Learning Research},
    issn={2835-8856},
    year={2024},
    url={https://openreview.net/forum?id=0T2OTVCCC1},
    note={}
}

Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs

@misc{krach2024learningchaoticsystemslongterm,
  title={Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs}, 
  author={Florian Krach and Josef Teichmann},
  year={2024},
  eprint={2407.18808},
  archivePrefix={arXiv},
  primaryClass={stat.ML},
  url={https://arxiv.org/abs/2407.18808},
}

Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs

@misc{heiss2024nonparametricfilteringestimationclassification,
    title={Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs}, 
    author={Jakob Heiss and Florian Krach and Thorsten Schmidt and Félix B. Tambe-Ndonfack},
    year={2024},
    eprint={2412.03271},
    archivePrefix={arXiv},
    primaryClass={stat.ML},
    url={https://arxiv.org/abs/2412.03271}, 
}

Neural Jump ODEs as Generative Models

@misc{crowell2025neuraljumpodesgenerative,
    title={Neural Jump ODEs as Generative Models}, 
    author={Robert A. Crowell and Florian Krach and Josef Teichmann},
    year={2025},
    eprint={2510.02757},
    archivePrefix={arXiv},
    primaryClass={stat.ML},
    url={https://arxiv.org/abs/2510.02757}, 
}

Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering

@inproceedings{
    herrera2021neural,
    title={Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering},
    author={Calypso Herrera and Florian Krach and Josef Teichmann},
    booktitle={International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=JFKR3WqwyXR}
}

Acknowledgements and References

This code is based on the code-repo of the first paper Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering: https://github.com/HerreraKrachTeichmann/NJODE
Parts of this code are based on and/or copied from the code of: https://github.com/edebrouwer/gru_ode_bayes, of the paper GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series and the code of: https://github.com/YuliaRubanova/latent_ode, of the paper Latent ODEs for Irregularly-Sampled Time Series and the code of: https://github.com/zcakhaa/DeepLOB-Deep-Convolutional-Neural-Networks-for-Limit-Order-Books/, of the paper DeepLOB: Deep convolutional neural networks for limit order books.

The High Frequency Crypto Limit Order Book Data was made public in Kaggle. The other bitcoin LOB dataset was gratefully provided by Covario, but is not publicly available.

The GIFs of the training progress were generated with imageio:

Instructions for Running Experiments of Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs

The configs for the experiment are in the main config file config.py as well as in config_LOB.py, config_NJmodel.py and config_randomizedNJODE.py.

Dataset Generation

go to the source directory:

cd NJODE

Synthetic Datasets

generate Poisson Point Process dataset:

python data_utils.py --dataset_name=PoissonPointProcess --dataset_params=poisson_pp_dict

generate fractional Brownian Motion (FBM) datasets:

python data_utils.py --dataset_name=FBM --dataset_params=FBM_1_dict
python data_utils.py --dataset_name=FBM --dataset_params=FBM_2_dict
python data_utils.py --dataset_name=FBM --dataset_params=FBM_3_dict
python data_utils.py --dataset_name=FBM --dataset_params=FBM_4_dict

generate 2-dim correlated BM dataset:

python data_utils.py --dataset_name=BM2DCorr --dataset_params=BM_2D_dict

generate BM and its Square dataset:

python data_utils.py --dataset_name=BMandVar --dataset_params=BM_and_Var_dict
python data_utils.py --dataset_name=BM --dataset_params=BM_dict

generate BS with dependent observation intensity dataset:

python data_utils.py --dataset_name=BlackScholes --dataset_params=BS_dep_intensity_dict

generate double-pendulum (deterministic chaotic system) dataset:

python data_utils.py --dataset_name=DoublePendulum --dataset_params=DP_dict1
python data_utils.py --dataset_name=DoublePendulum --dataset_params=DP_dict2
python data_utils.py --dataset_name=DoublePendulum --dataset_params=DP_dict3
python data_utils.py --dataset_name=DoublePendulum --dataset_params=DP_dict3_test --seed=3

generate BM Filtering Problem dataset:

python data_utils.py --dataset_name=BMFiltering --dataset_params=BM_Filter_dict
python data_utils.py --dataset_name=BMFiltering --dataset_params=BM_Filter_dict_1
python data_utils.py --dataset_name=BMFiltering --dataset_params=BM_Filter_dict_testdata

generate BM TimeLag dataset:

python data_utils.py --dataset_name=BMwithTimeLag --dataset_params=BM_TimeLag_dict_1 --seed=3
python data_utils.py --dataset_name=BMwithTimeLag --dataset_params=BM_TimeLag_dict_2 --seed=3
python data_utils.py --dataset_name=BMwithTimeLag --dataset_params=BM_TimeLag_dict_testdata

generate BlackScholes dataset:

python data_utils.py --dataset_name=BlackScholes --dataset_params=BlackScholes_dict

Real World Datasets

generate Limit Order Book (LOB) dataset with raw data from Covario (ATTENTION: the needed raw data was provided by Covario but is not publicly available, hence this data generation will raise an error, unless you provide a working link to raw data):

python data_utils.py --dataset_name=LOB --dataset_params=LOB_dict1
python data_utils.py --dataset_name=LOB --dataset_params=LOB_dict1_2
python data_utils.py --dataset_name=LOB --dataset_params=LOB_dict2
python data_utils.py --dataset_name=LOB --dataset_params=LOB_dict3
python data_utils.py --dataset_name=LOB --dataset_params=LOB_dict3_2

generate Limit Order Book (LOB) dataset with raw data from Kaggle data:

python data_utils.py --dataset_name=LOB --dataset_params=LOB_dict_K_1
python data_utils.py --dataset_name=LOB --dataset_params=LOB_dict_K_1_2
python data_utils.py --dataset_name=LOB --dataset_params=LOB_dict_K_2
python data_utils.py --dataset_name=LOB --dataset_params=LOB_dict_K_3
python data_utils.py --dataset_name=LOB --dataset_params=LOB_dict_K_3_2
python data_utils.py --dataset_name=LOB --dataset_params=LOB_dict_K_4
python data_utils.py --dataset_name=LOB --dataset_params=LOB_dict_K_4_2
python data_utils.py --dataset_name=LOB --dataset_params=LOB_dict_K_5
python data_utils.py --dataset_name=LOB --dataset_params=LOB_dict_K_6
python data_utils.py --dataset_name=LOB --dataset_params=LOB_dict_K_6_2

REMARK: the physionet and climate datasets are automatically downloaded when training a model on them for the first time (=> do not train multiple models at parallel at first time, since otherwise each will start the download and overwrite each others output. after download is complete, parallel training can be used normally!)

Training & Testing

go to the source directory:

cd NJODE

(parallel) training of models:

python run.py --params=... --NB_JOBS=... --NB_CPUS=... --first_id=...

List of all flags:

params: name of the params list (defined in config.py) to use for parallel run
NB_JOBS: nb of parallel jobs to run with joblib
first_id: First id of the given list / to start training of
get_overview: name of the dict (defined in config.py) defining input for extras.get_training_overview
USE_GPU: whether to use GPU for training
ANOMALY_DETECTION: whether to run in torch debug mode
SEND: whether to send results via telegram
NB_CPUS: nb of CPUs used by each training
model_ids: List of model ids to run
DEBUG: whether to run parallel in debug mode
saved_models_path: path where the models are saved
overwrite_params: name of dict (defined in config.py) to use for overwriting params
plot_paths: name of the dict (in config.py) defining input for extras.plot_paths_from_checkpoint
climate_crossval: name of the dict (in config.py) defining input for extras.get_cross_validation
plot_conv_study: name of the dict (in config.py) defining input for extras.plot_convergence_study

Synthetic Datasets

See config.py:

train model on Poisson Point Process:

python run.py --params=param_list_poissonpp1 --NB_JOBS=1 --NB_CPUS=1 --first_id=1
python run.py --plot_paths=plot_paths_ppp_dict

train model on FBM:

python run.py --params=param_list_FBM1 --NB_JOBS=1 --NB_CPUS=1 --first_id=1  --get_overview=overview_dict_FBM1
python run.py --plot_paths=plot_paths_FBM_dict

train model on BM2DCorr:

python run.py --params=param_list_BM2D_1 --NB_JOBS=1 --NB_CPUS=1 --first_id=1  --get_overview=overview_dict_BM2D_1
python run.py --plot_paths=plot_paths_BM2D_dict

train model on BMandVar:

python run.py --params=param_list_BMandVar_1 --NB_JOBS=2 --NB_CPUS=1 --first_id=1
python run.py --plot_paths=plot_paths_BMVar_dict

train model on dependent observation intensity datasets:

python run.py --params=param_list_DepIntensity_1 --NB_JOBS=24 --NB_CPUS=1 --first_id=1
python run.py --plot_paths=plot_paths_DepIntensity_dict

train PD-NJODE on DoublePendulum:

python run.py --params=param_list_DP --NB_JOBS=40 --NB_CPUS=2 --first_id=1 --get_overview=overview_dict_DP
python run.py --plot_paths=plot_paths_DP_dict
python run.py --params=param_list_DP --NB_JOBS=40 --first_id=1 --saved_models_path="../data/saved_models_DoublePendulum/" --overwrite_params="{'test_data_dict': 'DP_dict3_test'}"
python run.py --plot_paths=plot_paths_DP_dict

train model on BM Filtering Problem dataset:

python run.py --params=param_list_BMFilter_1 --NB_JOBS=8 --NB_CPUS=2 --first_id=1 --get_overview=overview_dict_BMFilter_1
python run.py --plot_paths=plot_paths_BMFilter_dict

train model on BM with TimeLag dataset:

python run.py --params=param_list_BMTimeLag --NB_JOBS=8 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_BMTimeLag
python run.py --plot_paths=plot_paths_BMTimeLag_dict

train NJmodel on BS (see config_NJmodel.py):

python run.py --params=param_list_NJmodel --NB_JOBS=8 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_NJmodel
python run.py --plot_paths=plot_paths_NJmodel_dict

Real World Datasets

See config.py:

train model on PhysioNet datasets:

python run.py --params=param_list_physio --NB_JOBS=8 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_physio --crossval=crossval_dict_physio

train model on Climate datasets:

python run.py --params=param_list_climate --NB_JOBS=8 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_climate --crossval=crossval_dict_climate

See config_LOB.py:

train model on Limit Order Book datasets:

python run.py --params=param_list_LOB --NB_JOBS=22 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_LOB
python run.py --plot_paths=plot_paths_LOB_dict

retrain classifier on LOB dataset:

python run.py --params=param_list_retrainLOB --NB_JOBS=42 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_retrainLOB

train model on Limit Order Book Kaggle datasets:

python run.py --params=param_list_LOB_K --NB_JOBS=22 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_LOB_K
python run.py --params=param_list_LOB_n --NB_JOBS=22 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_LOB_n
python run.py --plot_paths=plot_paths_LOB_K_dict

retrain classifier on LOB Kaggle dataset:

python run.py --params=param_list_retrainLOB_K --NB_JOBS=42 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_retrainLOB_K
python run.py --params=param_list_retrainLOB_n --NB_JOBS=42 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_retrainLOB_n

training of the DeepLOB model from the paper DeepLOB: Deep Convolutional Neural Networks for Limit Order Books as comparison

# training of the DeepLOB model
python ../DeepLOB/train_DeepLOB.py

fitting of LinReg models as baselines on LOB data:

python LOB_linreg.py

Experimental: Randomized NJODE

In this section we provide instructions for running the experiments of the randomized NJODE model. In particular, this is the NJODE model, where only the (last layer of) the readout map is trained, while the neural-ODE and the encoder/jump network are kept fixed. There are two versions of the randomized NJODE model: one where the readout map is trained via SGD and one where it is trained via the closed-form solution of OLS (i.e. fitting a linear regression model).

See config_randomizedNJODE.py:

train randomizedNJODE on BM:

python run.py --params=param_list_randNJODE_1 --NB_JOBS=1 --NB_CPUS=3 --first_id=1
python run.py --params=param_list_randNJODE_2 --NB_JOBS=1 --NB_CPUS=3 --first_id=3

train randomizedNJODE on BS:

python run.py --params=param_list_randNJODE_BS --NB_JOBS=1 --NB_CPUS=4 --first_id=1 --get_overview=overview_dict_randNJODE_BS

train randomizedNJODE on DoublePendulum:

python run.py --params=param_list_randNJODE_3 --NB_JOBS=1 --NB_CPUS=3 --first_id=100

Experimental: learning quantiles with NJODE

Parameters defined in config_Quantiles.py.

get datasets:

python data_utils.py --dataset_name=BMandQuantiles --dataset_params=BM_Quantiles --seed=0
python data_utils.py --dataset_name=BMandQuantiles --dataset_params=BM_Quantiles_test --seed=1

train PD-NJODE:

python run.py --params=param_list_BMQ --NB_JOBS=16 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_BMQ
python run.py --plot_paths=plot_paths_BMQ_dict

Direct Variance Prediction with NJODE

Instead of predicting the variance via the first 2 moments, which can lead to numerical instabilities (and therefore implausible negative values or non positive-semi definite covariance matrices), we can also directly predict the marginal variance or the covariance matrix using the NJODE model. In particular, we define the model with two output parts $(Y,W)$, where $Y$ is the original output and $W$ is the variance/covariance output. Then we can train $Y$ with the standard loss function to approximate the conditional expectation of the process $X$ and train $V=W^2$ (in the marginal variance case) or $V=W^\top W$ (in the covariance matrix case) to approximate the process $(X-Y)^2$. By using the square $V$ of $W$ to approximate $(X-Y)^2$, we have a hard-coded way to avoid numerical instabilities (negative values of the variance or non positive-semi definite covariance matrices), since the network output $W$ corresponds to the standard deviation, which is squared to get the variance (in particular, $W$ can have negative entries, which are simply interpreted as positive). By the theoretical results, the model output $W$ or $V$, respectively, learns to approximate the conditional expectation of $(X-Y)^2$ arbitrarily well, which coincides with the conditional variance of $X$.
See the NJODE website for more details.

Parameters defined in config_uncertainty.py.

train NJODE on Physionet dataset with variance prediction:

python run.py --params=param_list_physio_U --NB_JOBS=25 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_physio_U
python run.py --plot_paths=eval_physio_U_dict

get datasets:

python data_utils.py --dataset_name=BM --dataset_params=BM_dict_U --seed=0

train NJODE on BM dataset with variance prediction:

python run.py --params=param_list_BMandVar_U --NB_JOBS=25 --NB_CPUS=1 --first_id=1

Instructions for Running Experiments of Generative NJODE

The code for the experiments of the paper Neural Jump ODEs as Generative Models.

The configs for these experiments are in config_GenNJODE.py.

Dataset Generation

go to the source directory:

cd NJODE

generate Geometric Brownian Motion (GBM) datasets:

python data_utils.py --dataset_params=GBM_joint_training_dict --seed=0
python data_utils.py --dataset_params=GBM_drift_lim_training_dict --seed=0
python data_utils.py --dataset_params=GBM_vol_lim_training_dict --seed=0
python data_utils.py --dataset_params=GBM_drift_training_dict --seed=0
python data_utils.py --dataset_params=GBM_vol_training_dict --seed=0

python data_utils.py --dataset_params=GBM_joint_training_dict2 --seed=0

generate Ornstein-Uhlenbeck (OU) dataset:

python data_utils.py --dataset_params=OU_joint_training_dict --seed=0

Training

train NJODE on GBM datasets:

python run.py --params=param_list_GenNJODE_GBM --NB_JOBS=10 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_GBM_genNJODE

train NJODE on OU datasets:

python run.py --params=param_list_GenNJODE_OU --NB_JOBS=1 --NB_CPUS=1 --first_id=1

Generation of paths

generate paths with trained models for GBM:

# joint instantaneous coedfficient estimation
python generate.py --params=generation_dict_GBM1_1 --NB_CPUS=1 --USE_GPU=False
python generate.py --params=generation_dict_GBM1_2 --NB_CPUS=1 --USE_GPU=False
python generate.py --params=generation_dict_GBM1_3 --NB_CPUS=1 --USE_GPU=False
python generate.py --params=generation_dict_GBM1_4 --NB_CPUS=1 --USE_GPU=False

# joint baseline coefficient estimation
python generate.py --params=generation_dict_GBM2_1 --NB_CPUS=1 --USE_GPU=False

# individual instantaneous coefficient estimation
python generate.py --params=generation_dict_GBM3_2 --NB_CPUS=1 --USE_GPU=False

# individual baseline coefficient estimation
python generate.py --params=generation_dict_GBM4_1 --NB_CPUS=1 --USE_GPU=False

generate paths with trained models for OU:

# joint instantaneous coedfficient estimation
python generate.py --params=generation_dict_OU1_2 --NB_CPUS=1 --USE_GPU=False

Instructions for Running Experiments of Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework

The code for the experiments of the paper Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework.

The configs for these experiments are in config_NJODE3.py.

Dataset Generation

go to the source directory:

cd NJODE

generate the BlackScholes datset with dependent observations

python data_utils.py --dataset_name=BlackScholes --dataset_params=BS_dep_obs_dict

generate the Brownian motion datset with noisy observations

python data_utils.py --dataset_name=BMNoisyObs --dataset_params=BM_NoisyObs_dict

Training & Testing

go to the source directory:

cd NJODE

train model on BlackScholes dataset with dependent observations

python run.py --params=param_list_DepObs_1 --NB_JOBS=24 --NB_CPUS=1 --first_id=1
python run.py --plot_paths=plot_paths_DepObs_dict

train model on BM dataset with noisy observations

python run.py --params=param_list_BM_NoisyObs --NB_JOBS=24 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_BM_NoisyObs
python run.py --plot_paths=plot_paths_BM_NoisyObs_dict

train model for noisy observations on PhysioNet datasets:

python run.py --params=param_list_physio_N3 --NB_JOBS=12 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_physio_N3 --plot_loss_comparison=plot_loss_comparison_physio_N3

train model for noisy observations on Climate datasets:

python run.py --params=param_list_climate_N3 --NB_JOBS=40 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_climate_N3 --crossval=crossval_dict_climate_N3

Instructions for Running Experiments of Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs

The code for the experiments of the paper Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs.

The configs for these experiments are in config_ODE.py.

generate datasets:

python data_utils.py --dataset_name=DoublePendulum --dataset_params=DP_dict3_2_test --seed=3
python data_utils.py --dataset_name=DoublePendulum --dataset_params=DP_dict4
python data_utils.py --dataset_name=DoublePendulum --dataset_params=DP_dict4_test --seed=3

python data_utils.py --dataset_name=BlackScholes --dataset_params=BS_LT_dict
python data_utils.py --dataset_name=BlackScholes --dataset_params=BS_LT_dict1
python data_utils.py --dataset_name=BlackScholes --dataset_params=BS_LT_dict_test --seed=3
python data_utils.py --dataset_name=BlackScholes --dataset_params=BS_LT_dict2
python data_utils.py --dataset_name=BlackScholes --dataset_params=BS_LT_dict2_test --seed=3

train PD-NJODE on DoublePendulum:

python run.py --params=param_list_ODE_DP --NB_JOBS=14 --NB_CPUS=2 --first_id=1 --get_overview=overview_dict_ODE_DP
python run.py --plot_paths=plot_paths_ODE_DP_dict

train model on BlackScholes dataset for long-term forecasting:

python run.py --params=param_list_BS_LT --NB_JOBS=4 --NB_CPUS=2 --first_id=1 --get_overview=overview_dict_BS_LT
python run.py --plot_paths=plot_paths_BS_LT_dict

train on physionet dataset for long-term forecasting:

python run.py --params=param_list_physioLT --NB_JOBS=8 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_physioLT # --crossval=crossval_dict_physio

Instructions for Running Experiments of Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs

The code for the experiments of the paper Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs.

The configs for these experiments are in config_ParamFilter.py.

generate datasets:

python data_utils.py --dataset_name=BlackScholesUncertainParams --dataset_params=PF_BSUP_dict1 --seed=0
python data_utils.py --dataset_name=BlackScholesUncertainParams --dataset_params=PF_BSUP_dict1_test --seed=1

python data_utils.py --dataset_name=BlackScholesUncertainParams --dataset_params=PF_BSUP_dict2 --seed=0
python data_utils.py --dataset_name=BlackScholesUncertainParams --dataset_params=PF_BSUP_dict2_test --seed=1

for i in 1 2 
do
  for j in {1..6}
  do
    python data_utils.py --dataset_name=BlackScholesUncertainParams --dataset_params=PF_BSUP_dict_CS${i}_${j} --seed=0
    python data_utils.py --dataset_name=BlackScholesUncertainParams --dataset_params=PF_BSUP_dict_CS${i}_test_${j} --seed=1
  done
done

python data_utils.py --dataset_name=BMwithUncertainDrift --dataset_params=PF_BMwUD_dict1 --seed=0
python data_utils.py --dataset_name=BMwithUncertainDrift --dataset_params=PF_BMwUD_dict1_test --seed=1

python data_utils.py --dataset_name=BMFiltering --dataset_params=IO_BM_Filter_dict_1 --seed=0
python data_utils.py --dataset_name=BMFiltering --dataset_params=IO_BM_Filter_dict_1_test --seed=1

python data_utils.py --dataset_name=BlackScholes --dataset_params=IO_BS_dict --seed=0
python data_utils.py --dataset_name=BlackScholes --dataset_params=IO_BS_dict_test --seed=1

python data_utils.py --dataset_name=CIRUncertainParams --dataset_params=PF_CIR_dict1 --seed=0
python data_utils.py --dataset_name=CIRUncertainParams --dataset_params=PF_CIR_dict1_test --seed=1
python data_utils.py --dataset_name=CIRUncertainParams --dataset_params=PF_CIR_dict2 --seed=0
python data_utils.py --dataset_name=CIRUncertainParams --dataset_params=PF_CIR_dict2_test --seed=1
python data_utils.py --dataset_name=CIRUncertainParams --dataset_params=PF_CIR_dict3 --seed=0
python data_utils.py --dataset_name=CIRUncertainParams --dataset_params=PF_CIR_dict3_test --seed=1
python data_utils.py --dataset_name=CIRUncertainParams --dataset_params=PF_CIR_dict4 --seed=0
python data_utils.py --dataset_name=CIRUncertainParams --dataset_params=PF_CIR_dict4_test --seed=1

python data_utils.py --dataset_name=BMClassification --dataset_params=IO_BMClass_dict --seed=0
python data_utils.py --dataset_name=BMClassification --dataset_params=IO_BMClass_dict_test --seed=1

train NJODE:

python run.py --params=param_list_PF_BSUP --NB_JOBS=32 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_PF_BSUP

python run.py --params=param_list_PF_BSUP2 --NB_JOBS=32 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_PF_BSUP2
python run.py --plot_paths=plot_paths_PF_BSUP_dict2
python run.py --plot_paths=plot_paths_PF_BSUP_dict2_1

python run.py --params=param_list_PF_BSUP_CS --NB_JOBS=32 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_PF_BSUP_CS
python run.py --plot_paths=plot_paths_PF_BSUP_CS_dict

python run.py --params=param_list_PF_BMwUD --NB_JOBS=32 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_PF_BMwUD
python run.py --plot_paths=plot_paths_PF_BMwUD_dict

python run.py --params=param_list_IO_BMFilter --NB_JOBS=32 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_IO_BMFilter
python run.py --plot_paths=plot_paths_IO_BMFilter_dict

python run.py --params=param_list_IO_BS_LJ --NB_JOBS=32 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_IO_BS_LJ
python run.py --plot_losses=plot_loss_IO_BS_LJ

python run.py --params=param_list_PF_CIR --NB_JOBS=32 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_PF_CIR
python run.py --plot_paths=plot_paths_PF_CIR_dict

python run.py --params=param_list_IO_BMClass --NB_JOBS=16 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_IO_BMClass
python run.py --plot_paths=plot_paths_IO_BMClass_dict

Instructions for Running Experiments of Neural Jump Ordinary Differential Equations

The original repository of the paper Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering is available here. In the current repository, the code was developed further such that it is more user-friendly. Below are the instructions to run the experiments of the first paper.

The configs for these experiments are in config_NJODE1.py.

Dataset Generation

go to the source directory:

cd NJODE

generate the standard BlackScholes, Heston and OrnsteinUhlenbeck datasets:

python data_utils.py --dataset_name=BlackScholes --dataset_params=hyperparam_default
python data_utils.py --dataset_name=Heston --dataset_params=hyperparam_default
python data_utils.py --dataset_name=OrnsteinUhlenbeck --dataset_params=hyperparam_default

generate dataset of Heston model without Feller condition:

python data_utils.py --dataset_name=HestonWOFeller --dataset_params=HestonWOFeller_dict1
python data_utils.py --dataset_name=HestonWOFeller --dataset_params=HestonWOFeller_dict2

generate combined dataset OU+BS:

python data_utils.py --dataset_name=combined_OrnsteinUhlenbeck_BlackScholes --dataset_params=combined_OU_BS_dataset_dicts

generate sine-drift BS dataset OU+BS:

python data_utils.py --dataset_name=sine_BlackScholes --dataset_params=sine_BS_dataset_dict1
python data_utils.py --dataset_name=sine_BlackScholes --dataset_params=sine_BS_dataset_dict2

Training & Testing

go to the source directory:

cd NJODE

train models on the 3 standard datasets (Black-Scholes, Heston, Ornstein-Uhlenbeck):

python run.py --params=params_list1 --NB_JOBS=3 --NB_CPUS=1 --first_id=1

run the convergence study on the 3 standard datasets:

# Black-Scholes
python run.py --params=params_list_convstud_BS --NB_JOBS=32 --NB_CPUS=1 --first_id=1 --plot_conv_study=plot_conv_stud_BS_dict1
python run.py --plot_conv_study=plot_conv_stud_BS_dict2

# Heston
python run.py --params=params_list_convstud_Heston --NB_JOBS=32 --NB_CPUS=1 --first_id=1 --plot_conv_study=plot_conv_stud_heston_dict1
python run.py --plot_conv_study=plot_conv_stud_heston_dict2

# Ornstein-Uhlenbeck
python run.py --params=params_list_convstud_OU --NB_JOBS=32 --NB_CPUS=1 --first_id=1 --plot_conv_study=plot_conv_stud_OU_dict1
python run.py --plot_conv_study=plot_conv_stud_OU_dict2

train model on HestonWOFeller dataset:

python run.py --params=params_list_HestonWOFeller --NB_JOBS=2 --NB_CPUS=1 --first_id=1

train model on Combined OU+BS dataset (regime switch)

python run.py --params=params_list_combined_OU_BS --NB_JOBS=1 --NB_CPUS=1 --first_id=1

train model on sine-drift BS dataset (explicit time dependence)

python run.py --params=params_list_sine_BS --NB_JOBS=2 --NB_CPUS=1 --first_id=1

train the GRU-ODE-Bayes model on the 3 standard datasets:

python run.py --params=params_list_GRUODEBayes --NB_JOBS=32 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_GRUODEBayes

train model on climate dataset of GRU-ODE-Bayes:

python run.py --params=params_list_NJODE1_climate --NB_JOBS=32 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_NJODE1_climate --crossval=crossval_dict_NJODE1_climate

train model on Physionet Dataset of Latent ODEs for Irregularly-Sampled Time Series:

python run.py --params=params_list_NJODE1_physionet --NB_JOBS=32 --NB_CPUS=1 --first_id=1 --get_overview=overview_dict_NJODE1_physionet --crossval=crossval_dict_NJODE1_physionet

Changes and potantial backward incompatibilities

Changed naming of eval_loss -> val_loss, eval_time -> val_time and which_eval_loss -> which_val_loss. This might cause problems when loading old models, where the metric_id-x.csv files use the old column naming. To fix this, one can simply rename the columns in the csv files to the new naming. Moreover, when loading old models where which_eval_loss was specified, one has to change this to which_val_loss in the saved model_overview.csv file. This can be done with overwriting params option.

Name		Name	Last commit message	Last commit date
Latest commit History 42 Commits
NJODE		NJODE
baselines		baselines
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
requirements.txt		requirements.txt

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Neural Jump ODEs

Requirements

Usage, License & Citation

Acknowledgements and References

Instructions for Running Experiments of Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs

Dataset Generation

Synthetic Datasets

Real World Datasets

Training & Testing

Synthetic Datasets

Real World Datasets

Experimental: Randomized NJODE

Experimental: learning quantiles with NJODE

Direct Variance Prediction with NJODE

Instructions for Running Experiments of Generative NJODE

Dataset Generation

Training

Generation of paths

Instructions for Running Experiments of Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework

Dataset Generation

Training & Testing

Instructions for Running Experiments of Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs

Instructions for Running Experiments of Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs

Instructions for Running Experiments of Neural Jump Ordinary Differential Equations

Dataset Generation

Training & Testing

Changes and potantial backward incompatibilities

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Repository files navigation

Neural Jump ODEs

Requirements

Usage, License & Citation

Acknowledgements and References

Instructions for Running Experiments of Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs

Dataset Generation

Synthetic Datasets

Real World Datasets

Training & Testing

Synthetic Datasets

Real World Datasets

Experimental: Randomized NJODE

Experimental: learning quantiles with NJODE

Direct Variance Prediction with NJODE

Instructions for Running Experiments of Generative NJODE

Dataset Generation

Training

Generation of paths

Instructions for Running Experiments of Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework

Dataset Generation

Training & Testing

Instructions for Running Experiments of Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs

Instructions for Running Experiments of Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs

Instructions for Running Experiments of Neural Jump Ordinary Differential Equations

Dataset Generation

Training & Testing

Changes and potantial backward incompatibilities

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