ImputeGAP is a comprehensive Python library for imputation of missing values in time series data. It implements user-friendly APIs to easily visualize, analyze, and repair time series datasets. The library supports a diverse range of imputation algorithms and modular missing data simulation catering to datasets with varying characteristics. ImputeGAP includes extensive customization options, such as automated hyperparameter tuning, benchmarking, explainability, and downstream evaluation.
In detail, the package provides:
- Access to commonly used datasets in the time series imputation field (Datasets).
- Configurable contamination that simulates real-world missingness patterns (Patterns).
- Parameterizable state-of-the-art time series imputation algorithms (Algorithms).
- Extensive benchmarking to compare the performance of imputation algorithms (Benchmark).
- Modular tools to assess the impact of imputation on key downstream tasks (Downstream).
- Fine-grained analysis of the impact of time series features on imputation results (Explainer).
- Seamless integration of new algorithms in Python, C++, Matlab, Java, and R (Integration).
If you like our library, please add a ⭐ in our GitHub repository.
| Tools | URL |
|---|---|
| 📚 Documentation | https://imputegap.readthedocs.io/ |
| 📦 PyPI | https://pypi.org/project/imputegap/ |
| 📁 Datasets | Description |
| Family | Algorithm | Venue -- Year |
|---|---|---|
| Deep Learning | BitGraph [32] | ICLR -- 2024 |
| Deep Learning | BayOTIDE [30] | PMLR -- 2024 |
| Deep Learning | MissNet [27] | KDD -- 2024 |
| Deep Learning | MPIN [25] | PVLDB -- 2024 |
| Deep Learning | PRISTI [26] | ICDE -- 2023 |
| Deep Learning | GRIN [29] | ICLR -- 2022 |
| Deep Learning | HKMF_T [31] | TKDE -- 2021 |
| Deep Learning | DeepMVI [24] | PVLDB -- 2021 |
| Deep Learning | MRNN [22] | IEEE Trans on BE -- 2019 |
| Deep Learning | BRITS [23] | NeurIPS -- 2018 |
| Deep Learning | GAIN [28] | ICML -- 2018 |
| Matrix Completion | CDRec [1] | KAIS -- 2020 |
| Matrix Completion | TRMF [8] | NeurIPS -- 2016 |
| Matrix Completion | GROUSE [3] | PMLR -- 2016 |
| Matrix Completion | ROSL [4] | CVPR -- 2014 |
| Matrix Completion | SoftImpute [6] | JMLR -- 2010 |
| Matrix Completion | SVT [7] | SIAM J. OPTIM -- 2010 |
| Matrix Completion | SPIRIT [5] | VLDB -- 2005 |
| Matrix Completion | IterativeSVD [2] | BIOINFORMATICS -- 2001 |
| Pattern Search | TKCM [11] | EDBT -- 2017 |
| Pattern Search | STMVL [9] | IJCAI -- 2016 |
| Pattern Search | DynaMMo [10] | KDD -- 2009 |
| Machine Learning | IIM [12] | ICDE -- 2019 |
| Machine Learning | XGBOOST [13] | KDD -- 2016 |
| Machine Learning | MICE [14] | Statistical Software -- 2011 |
| Machine Learning | MissForest [15] | BioInformatics -- 2011 |
| Statistics | KNNImpute | - |
| Statistics | Interpolation | - |
| Statistics | MinImpute | - |
| Statistics | ZeroImpute | - |
| Statistics | MeanImpute | - |
| Statistics | MeanImputeBySeries | - |
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Deployment
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Code Snippets
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Additional Information
ImputeGAP is compatible with Python>=3.10 (except 3.13) and Unix-compatible environment.
To create and set up an environment with Python 3.12, please refer to the installation guide.
To install the latest version of ImputeGAP from PyPI, run the following command:
pip install imputegapAlternatively, you can install the library from source:
git init
git clone https://github.com/eXascaleInfolab/ImputeGAP
cd ./ImputeGAP
pip install -e .ImputeGAP comes with several time series datasets. The list of datasets is described here.
As an example, we use the eeg-alcohol dataset, composed of individuals with a genetic predisposition to alcoholism. The dataset contains measurements from 64 electrodes placed on subject’s scalps, sampled at 256 Hz. The dimensions of the dataset are 64 series, each containing 256 values.
You can find this example of normalization in the file runner_loading.py.
To load and plot the eeg-alcohol dataset from the library:
from imputegap.recovery.manager import TimeSeries
from imputegap.tools import utils
# initialize the time series object
ts = TimeSeries()
print(f"ImputeGAP datasets : {ts.datasets}")
# load and normalize the dataset from file or from the code
ts.load_series(utils.search_path("eeg-alcohol"))
ts.normalize(normalizer="z_score")
# plot and print a subset of time series
ts.plot(input_data=ts.data, nbr_series=9, nbr_val=100, save_path="./imputegap_assets")
ts.print(nbr_series=9, nbr_val=20)The module ts.datasets contains all the publicly available datasets provided by the library, which can be listed as follows:
from imputegap.recovery.manager import TimeSeries
ts = TimeSeries()
print(f"ImputeGAP datasets : {ts.datasets}")We now describe how to simulate missing values in the loaded dataset. ImputeGAP implements eight different missingness patterns. For more details about the patterns, please refer to the documentation on this page.
You can find this example in the file runner_contamination.py.
As example, we show how to contaminate the eeg-alcohol dataset with the MCAR pattern:
from imputegap.recovery.manager import TimeSeries
from imputegap.tools import utils
# initialize the time series object
ts = TimeSeries()
# load and normalize the dataset
ts.load_series(utils.search_path("eeg-alcohol"))
ts.normalize(normalizer="z_score")
# contaminate the time series with MCAR pattern
ts_m = ts.Contamination.mcar(ts.data, rate_dataset=0.2, rate_series=0.4, block_size=10, seed=True)
# [OPTIONAL] plot the contaminated time series
ts.plot(ts.data, ts_m, nbr_series=9, subplot=True, save_path="./imputegap_assets/contamination")All missingness patterns developed in ImputeGAP are available in the ts.patterns module. They can be listed as follows:
from imputegap.recovery.manager import TimeSeries
ts = TimeSeries()
print(f"Missingness patterns : {ts.patterns}")In this section, we will illustrate how to impute the contaminated time series. Our library implements five families of imputation algorithms: Statistical, Machine Learning, Matrix Completion, Deep Learning, and Pattern Search. The list of algorithms is described here.
You can find this example in the file runner_imputation.py.
Let's illustrate the imputation using the CDRec algorithm from the Matrix Completion family.
from imputegap.recovery.imputation import Imputation
from imputegap.recovery.manager import TimeSeries
from imputegap.tools import utils
# initialize the time series object
ts = TimeSeries()
# load and normalize the dataset
ts.load_series(utils.search_path("eeg-alcohol"))
ts.normalize(normalizer="z_score")
# contaminate the time series
ts_m = ts.Contamination.mcar(ts.data)
# impute the contaminated series
imputer = Imputation.MatrixCompletion.CDRec(ts_m)
imputer.impute()
# compute and print the imputation metrics
imputer.score(ts.data, imputer.recov_data)
ts.print_results(imputer.metrics)
# plot the recovered time series
ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, algorithm=imputer.algorithm, save_path="./imputegap_assets/imputation")Imputation can be performed using either default values or user-defined values. To specify the parameters, please use a dictionary in the following format:
config = {"rank": 5, "epsilon": 0.01, "iterations": 100}
imputer.impute(params=config)All algorithms developed in ImputeGAP are available in the ts.algorithms module, which can be listed as follows:
from imputegap.recovery.manager import TimeSeries
ts = TimeSeries()
print(f"Imputation algorithms : {ts.algorithms}")The Optimizer component manages algorithm configuration and hyperparameter tuning. The parameters are defined by providing a dictionary containing the ground truth, the chosen optimizer, and the optimizer's options. Several search algorithms are available, including those provided by Ray Tune.
You can find this example in the file runner_optimization.py.
Let's illustrate the imputation using the CDRec algorithm and Ray-Tune AutoML:
from imputegap.recovery.imputation import Imputation
from imputegap.recovery.manager import TimeSeries
from imputegap.tools import utils
# initialize the time series object
ts = TimeSeries()
# load and normalize the dataset
ts.load_series(utils.search_path("eeg-alcohol"))
ts.normalize(normalizer="z_score")
# contaminate and impute the time series
ts_m = ts.Contamination.mcar(ts.data)
imputer = Imputation.MatrixCompletion.CDRec(ts_m)
# use Ray Tune to fine tune the imputation algorithm
imputer.impute(user_def=False, params={"input_data": ts.data, "optimizer": "ray_tune"})
# compute the imputation metrics with optimized parameter values
imputer.score(ts.data, imputer.recov_data)
# compute the imputation metrics with default parameter values
imputer_def = Imputation.MatrixCompletion.CDRec(ts_m).impute()
imputer_def.score(ts.data, imputer_def.recov_data)
# print the imputation metrics with default and optimized parameter values
ts.print_results(imputer_def.metrics, text="Default values")
ts.print_results(imputer.metrics, text="Optimized values")
# plot the recovered time series
ts.plot(input_data=ts.data, incomp_data=ts_m, recov_data=imputer.recov_data, nbr_series=9, subplot=True, algorithm=imputer.algorithm, save_path="./imputegap_assets/imputation")
# save hyperparameters
utils.save_optimization(optimal_params=imputer.parameters, algorithm=imputer.algorithm, dataset="eeg-alcohol", optimizer="ray_tune")All optimizers developed in ImputeGAP are available in the ts.optimizers module, which can be listed as follows:
from imputegap.recovery.manager import TimeSeries
ts = TimeSeries()
print(f"AutoML Optimizers : {ts.optimizers}")ImputeGAP can serve as a common test-bed for comparing the effectiveness and efficiency of time series imputation algorithms[33] . Users have full control over the benchmark by customizing various parameters, including the list of the algorithms to compare, the optimizer, the datasets to evaluate, the missingness patterns, the range of missing values, and the performance metrics.
You can find this example in the file runner_benchmark.py.
The benchmarking module can be utilized as follows:
from imputegap.recovery.benchmark import Benchmark
my_algorithms = ["SoftImpute", "KNNImpute"]
my_opt = ["default_params"]
my_datasets = ["eeg-alcohol"]
my_patterns = ["mcar"]
range = [0.05, 0.1, 0.2, 0.4, 0.6, 0.8]
my_metrics = ["*"]
# launch the evaluation
bench = Benchmark()
bench.eval(algorithms=my_algorithms, datasets=my_datasets, patterns=my_patterns, x_axis=range, metrics=my_metrics, optimizers=my_opt)You can enable the optimizer using the following command:
opt = {"optimizer": "ray_tune", "options": {"n_calls": 1, "max_concurrent_trials": 1}}
my_opt = [opt]ImputeGAP includes a dedicated module for systematically evaluating the impact of data imputation on downstream tasks. Currently, forecasting is the primary supported task, with plans to expand to additional applications in the future.
You can find this example in the file runner_downstream.py.
Below is an example of how to call the downstream process for the model Prophet by defining a dictionary for the evaluator and selecting the model:
from imputegap.recovery.imputation import Imputation
from imputegap.recovery.manager import TimeSeries
from imputegap.tools import utils
# initialize the time series object
ts = TimeSeries()
print(f"ImputeGAP downstream models for forcasting : {ts.forecasting_models}")
# load and normalize the dataset
ts.load_series(utils.search_path("forecast-economy"))
ts.normalize()
# contaminate the time series
ts_m = ts.Contamination.aligned(ts.data, rate_series=0.8)
# define and impute the contaminated series
imputer = Imputation.MatrixCompletion.CDRec(ts_m)
imputer.impute()
# compute and print the downstream results
downstream_config = {"task": "forecast", "model": "hw-add", "comparator": "ZeroImpute"}
imputer.score(ts.data, imputer.recov_data, downstream=downstream_config)
ts.print_results(imputer.downstream_metrics, algorithm=imputer.algorithm)All downstream models developed in ImputeGAP are available in the ts.forecasting_models module, which can be listed as follows:
from imputegap.recovery.manager import TimeSeries
ts = TimeSeries()
print(f"ImputeGAP downstream models for forecasting : {ts.forecasting_models}")The library provides insights into the algorithm’s behavior by identifying the features that impact the imputation results. It trains a regression model to predict imputation results across various methods and uses SHapley Additive exPlanations (SHAP) to reveal how different time series features influence the model’s predictions.
You can find this example in the file runner_explainer.py.
Let’s illustrate the explainer using the CDRec algorithm and MCAR missingness pattern:
from imputegap.recovery.manager import TimeSeries
from imputegap.recovery.explainer import Explainer
from imputegap.tools import utils
# initialize the time series and explainer object
ts = TimeSeries()
exp = Explainer()
# load and normalize the dataset
ts.load_series(utils.search_path("eeg-alcohol"))
ts.normalize(normalizer="z_score")
# configure the explanation
exp.shap_explainer(input_data=ts.data, extractor="pycatch", pattern="mcar", file_name=ts.name, algorithm="CDRec")
# print the impact of each feature
exp.print(exp.shap_values, exp.shap_details)All feature extractors developed in ImputeGAP are available in the ts.extractors module, which can be listed as follows:
from imputegap.recovery.manager import TimeSeries
ts = TimeSeries()
print(f"ImputeGAP features extractors : {ts.extractors}")To add your own imputation algorithm, please refer to the detailed integration guide.
If you use ImputeGAP in your research, please cite the paper:
@article{nater2025imputegap,
title = {ImputeGAP: A Comprehensive Library for Time Series Imputation},
author = {Nater, Quentin and Khayati, Mourad and Pasquier, Jacques},
year = {2025},
eprint = {2503.15250},
archiveprefix = {arXiv},
primaryclass = {cs.LG},
url = {https://arxiv.org/abs/2503.15250}
}
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| Quentin Nater | Mourad Khayati |
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