E2GAN: End-to-End Generative Adversarial Network or Multivariate Time Series Imputation
The missing values, appear in most of multivariate time series, prevent advanced analysis of multivariate time series data. Existing imputation approaches try to deal with missing values by deletion, statistical imputation, machine learning based imputation and generative imputation. However, these methods are either incapable of dealing with temporal information or multi-stage. This paper proposes an end-to-end generative model E2GAN to impute missing values in multivariate time series. With the help of the discriminative loss and the squared error loss, E2GAN can imputethe incomplete time series by the nearest generated complete time series at one stage. Experiments on multiple real-world datasets show that our model outperforms the baselines on the imputation accuracy and achieves state-of-the-art classification/regression results on the downstream applications. Additionally, our method also gains better time efficiency than multi-stage method on the training of neural networks.
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Datasets
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Multivariate Time Series Imputation | KDD CUP Challenge 2018 | E^2GAN | MSE (10% missing) | 0.334 | # 1 |
