Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data
Authors: YongKyung Oh, Dongyoung Lim, Sungil Kim
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To assess the effectiveness of our approach, we conduct extensive experiments on four benchmark datasets for interpolation, forecasting, and classification tasks, and analyze the robustness of our methods with 30 public datasets under different missing rates. Our results demonstrate the efficacy of the proposed method in handling real-world irregular time series data. |
| Researcher Affiliation | Academia | Ulsan National Institute of Science and Technology, Republic of Korea {yongkyungoh, dlim, sungil.kim}@unist.ac.kr |
| Pseudocode | Yes | Algorithm 1 Train procedure for classification task |
| Open Source Code | Yes | Code is available at https://github.com/yongkyung-oh/Stable-Neural-SDEs. |
| Open Datasets | Yes | The Physio Net Mortality dataset contains multivariate time series data from 37 variables of Intensive Care Unit (ICU) records... (Silva et al., 2012); The Mu Jo Co (Tassa et al., 2018) dataset; The Physio Net Sepsis (Reyna et al., 2019) dataset; The Speech Commands (Warden, 2018) dataset; 30 datasets from the University of East Anglia (UEA) and the University of California Riverside (UCR) Time Series Classification Repository (Bagnall et al., 2018). |
| Dataset Splits | Yes | The data was divided into train, validation, and test sets in a 0.70/0.15/0.15 ratio. [...] Divide training data into a train set Dtrain and a validation set Dval. [...] ceasing the training when the validation loss didn t improve for 10 successive epochs. |
| Hardware Specification | Yes | Our experiments were performed using a server on Ubuntu 22.04 LTS, equipped with an Intel(R) Xeon(R) Gold 6242 CPU and six NVIDIA A100 40GB GPUs. |
| Software Dependencies | No | The paper mentions using 'python library torchsde', 'Python library torchcde', and 'Python library ray' but does not specify their version numbers. |
| Experiment Setup | Yes | For the proposed methodology, the training process spans 300 epochs, employing a batch size of 64 and a learning rate of 0.001. Hyperparameter optimization is conducted through a grid search, focusing on the number of layers in vector field nl of {16, 32, 64, 128} and hidden vector dimensions nh of {16, 32, 64, 128}. |