E²GAN: End-to-End Generative Adversarial Network for Multivariate Time Series Imputation
Authors: Yonghong Luo, Ying Zhang, Xiangrui Cai, Xiaojie Yuan
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. In this section, the proposed method is evaluated on two realworld datasets. The experimental results are analyzed and compared in details. |
| Researcher Affiliation | Academia | 1College of Computer Science, Nankai University, Tianjin, China 2College of Cyber Science, Nankai Univeristy, Tianjin, China |
| Pseudocode | No | The paper describes its methodology using mathematical formulations and textual explanations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Physio Net Challenge 2012 dataset (Physio Net). The Physio Net dataset, a public 80.67% missing medical dataset provided by [Silva et al., 2012]... KDD CUP 2018 Dataset (KDD).1 The KDD dataset (15% missing) is a public meteorologic dataset that comes from the KDD CUP Challenge 2018. 1KDD CUP. Available on: http://www.kdd.org/kdd2018/, 2018. |
| Dataset Splits | Yes | For all the experiments, we select 10% of dataset as validation set and another 10% as test set. |
| Hardware Specification | No | The paper mentions fixing 'the hardware' for time efficiency comparisons but does not specify any exact GPU/CPU models, processor types, or detailed computer specifications used for running experiments. |
| Software Dependencies | No | The paper mentions using the 'ADAM algorithm' for training networks but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The hidden units number of all GRUI cells are 64. The dimension of low-dimensional vector z is also 64. The dropout rates of our experiments are always fixed with 0.5. Our parameters for the KDD dataset are: epoch is 15, batch size is 16, learning rate is 0.005, pretrain epoch is 10, λ is 2. The parameters of Physio Net are: epoch is 10, learning rate is 0.005, λ is 50, pretrain epoch is 5. |