Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs
Authors: Ilan Naiman, N. Benjamin Erichson, Pu Ren, Michael W. Mahoney, Omri Azencot
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we detail the results of our extensive experiments. Details related to datasets and baselines are provided in App. E. Our code is available at Git Hub. |
| Researcher Affiliation | Academia | Ilan Naiman Ben-Gurion University, UC Berkeley naimani@post.bgu.ac.il N. Benjamin Erichson LBNL and ICSI erichson@lbl.gov Pu Ren LBNL pren@lbl.gov Michael W. Mahoney ICSI, LBNL, and UC Berkeley mmahoney@stat.berkeley.edu Omri Azencot Ben-Gurion University azencot@cs.bgu.ac.il |
| Pseudocode | No | The paper describes the steps of its model and training objective in text, but it does not include a distinct figure, block, or section labeled "Pseudocode" or "Algorithm" with structured steps. |
| Open Source Code | Yes | Our code is available at Git Hub. |
| Open Datasets | Yes | Energy, is a multivariate UCI appliance energy prediction dataset (Candanedo, 2017)... Finally, Mu Jo Co (Multi-Joint dynamics with Contact) (Todorov et al., 2012)... The dataset in this task comprises temperature at 2-meter height above the surface from ERA5 reanalysis dataset (Hersbach et al., 2020). |
| Dataset Splits | No | We split the train and test sets with a ratio of 80% and 20%. (Appendix G). This explicitly states train/test splits for the weather data, but a validation split is not explicitly mentioned for all experiments or any general case. |
| Hardware Specification | Yes | The software environments we use are: Cent OS Linux 7 (Core) and PYTHON 3.9.16, and the hardware is: NVIDIA RTX 3090. |
| Software Dependencies | Yes | The software environments we use are: Cent OS Linux 7 (Core) and PYTHON 3.9.16, and the hardware is: NVIDIA RTX 3090. |
| Experiment Setup | Yes | Combining the penalties from Sec. 3 and the loss Eq. 7, we arrive at the following training objective which includes a reconstruction term, a prediction term, and a regularization term, L = Ez1:T q[log p(xt1:t N |z1:T )] + αLpred(z1:T , z1:T ) βKL[q(z1:T |xt1:t N ) p(z1:T )] , (8) where α, β R+ are user weights... We also explore how stable our model is to hyperparameter choice. To this end, we perform an extensive grid search over the following space for: α, β {1.0, 0.9, 0.7, 0.5, 0.3, 0.1, 0.09, 0.07, 0.05, 0.03, 0.01, 0.009, 0.007, 0.005, 0.003, 0.001, 0.0009, 0.0007, 0.0005}2 for the stocks dataset. (Appendix J). |