Awesome

GAN_RNN_TS

Missing values distribution

Author: Ivan Bongiorni, Data Scientist. LinkedIn.

Convolutional Recurrent Seq2seq GAN for the Imputation of Missing Values in Time Series Data

Description

The goal of this project is the implementation of multiple configurations of a Recurrent Convolutional Seq2seq neural network for the imputation of time series data. Three implementations are provided:

A Recurrent Convolutional seq2seq model.
A GAN (Generative Adversarial Network) based on the same architecture above, where an Imputer is trained to fool an adversarial Network that tries to distinguish real and fake (imputed) time series.
A partially adversarial model, in which both Loss structures of previous models are combined in one: an Imputer model must reduce true error Loss, while trying to fool a Discriminator at the same time.

Models are Implemented in TensorFlow 2 and trained on the Wikipedia Web Traffic Time Series Forecasting dataset.

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Files

config.yaml: configuration parameters for data preprocessing, training and testing.

Pipelines:

main_processing.py: starts data preprocessing pipeline. Its outcomes are ready-to-train datasets saved in .npy (numpy) format in /data_processed/ folder.
main_train.py: starts training pipeline. Trained model is saved in /saved_models/ folder, with the 'model_name' provided in config.yaml.

Scripts:

tools.py: contains more technical functions that are iterated during preprocessing pipeline.
model.py: implementation of models' architectures.
train.py: contains functions for all training configurations.
deterioration.py: the script contains the function that calls an artificial deterioration of training data, in order to check imputation performance.
impute.py: final script, to be called in order to produce imputed data (for raw time series that contain NaN's) and export them for future projects.

Notebooks and explanations:

how_it_works.md: contains explanation of Deep Learning models in greater detail.
nan_exploration.ipynb: contains a study of the distribution of NaN's in the raw dataset, that lead to the development of the deterioration function.
data_scaling_exploration.ipynb: contains visualizations of the scaling function I employed in data preprocessing phase.
imputation_visual_check.ipynb: visualization of a models performance. The notebook loads the trained model specified in params['model_name'] and check its performance on Validation and Test data.
performance_comparison.ipynb: shows the performances of three trained models on Test data, compared.

Folders:

data_raw/: it is supposed to contain the raw Wikipedia Web Traffic Time Series Forecasting dataset, as it is downloaded (and unzipped) from Kaggle.
data_processed/: it contains the outcome of preprocesing pipeline, launched from main_processing.py. Observations will be stored in three sub-directories for Training/, Validation/ and Test/.
saved_models/: where models are saved at the end of training pipepine. Model names can be changed in config.yaml. In case a GAN is trained and config parameter save_discriminator is set to True, the Discriminator model will be saved as [model_name]_discriminator.h5.

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Modules required

langdetect==1.0.8
numpy==1.18.3
pandas==1.0.3
scikit-learn==0.22.2.post1
scipy==1.4.1
tensorflow==2.1.0

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Bibliography

Luo, Y., Cai, X., Zhang, Y., & Xu, J. (2018). Multivariate time series imputation with generative adversarial networks. In Advances in Neural Information Processing Systems (pp. 1596-1607).
Yoon, J., Jordon, J., & Van Der Schaar, M. (2018). Gain: Missing data imputation using generative adversarial nets. arXiv preprint arXiv:1806.02920.
Guo, Z., Wan, Y., & Ye, H. (2019). A data imputation method for multivariate time series based on generative adversarial network. Neurocomputing, 360, 185-197.
Liu, Y., Yu, R., Zheng, S., Zhan, E., & Yue, Y. (2019). NAOMI: Non-autoregressive multiresolution sequence imputation. In Advances in Neural Information Processing Systems (pp. 11238-11248).
Luo, Y., Zhang, Y., Cai, X., & Yuan, X. (2019, August). E2GAN: End-to-End Generative Adversarial Network for Multivariate Time Series Imputation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (pp. 3094-3100). AAAI Press.
Suo, Q., Yao, L., Xun, G., Sun, J., & Zhang, A. (2019, June). Recurrent Imputation for Multivariate Time Series with Missing Values. In 2019 IEEE International Conference on Healthcare Informatics (ICHI) (pp. 1-3). IEEE.
Tang, X., Yao, H., Sun, Y., Aggarwal, C. C., Mitra, P., & Wang, S. (2020). Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values. In AAAI (pp. 5956-5963).
Zhang, J., Mu, X., Fang, J., & Yang, Y. (2019). Time Series Imputation via Integration of Revealed Information Based on the Residual Shortcut Connection. IEEE Access, 7, 102397-102405.
Fortuin, V., Baranchuk, D., Rätsch, G., & Mandt, S. (2020, June). GP-VAE: Deep Probabilistic Time Series Imputation. In International Conference on Artificial Intelligence and Statistics (pp. 1651-1661).
Huang, T., Chakraborty, P., & Sharma, A. (2020). Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images. arXiv preprint arXiv:2005.04188.
Huang, Y., Tang, Y., VanZwieten, J., & Liu, J. (2020). Reliable machine prognostic health management in the presence of missing data. Concurrency and Computation: Practice and Experience, e5762.
Jun, E., Mulyadi, A. W., Choi, J., & Suk, H. I. (2020). Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction. arXiv preprint arXiv:2003.00655.
Qi, M., Qin, J., Wu, Y., & Yang, Y. (2020). Imitative Non-Autoregressive Modeling for Trajectory Forecasting and Imputation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12736-12745).
Wang, Y., Menkovski, V., Wang, H., Du, X., & Pechenizkiy, M. (2020). Causal Discovery from Incomplete Data: A Deep Learning Approach. arXiv preprint arXiv:2001.05343.
Yi, J., Lee, J., Kim, K. J., Hwang, S. J., & Yang, E. (2019). Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks. arXiv preprint arXiv:1906.00150.
Yoon, S., & Sull, S. (2020). GAMIN: Generative Adversarial Multiple Imputation Network for Highly Missing Data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8456-8464).

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Hardware

I trained this model on a fairly powerful machine: a System76 Adder WS laptop with 64 GB of RAM and NVidia RTX 2070 GPU.