Generative Time-series Modeling with Fourier Flows
Authors: Ahmed Alaa, Alex James Chan, Mihaela van der Schaar
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that Fourier flows perform competitively compared to state-of-the-art baselines. |
| Researcher Affiliation | Academia | Ahmed M. Alaa University of California, Los Angeles, USA ahmedmalaa@ucla.edu Alex J. Chan University of Cambridge, UK ajc340@cam.ac.uk Mihaela van der Schaar University of Cambridge, UK University of California, Los Angeles, USA Cambridge Center for AI in Medicine, UK The Alan Turing Institute, UK mv472@cam.ac.uk |
| Pseudocode | No | The paper does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We conduct experiments on: Google stocks data, UCI Energy data set, and a longitudinal follow-up clinical data set for lung cancer patients. |
| Dataset Splits | No | The paper states using 1,000 synthetic time-series 'to train all baselines' but does not specify explicit train/validation/test splits. For real data, it mentions replicating the experimental setup from Yoon et al. (2019) but does not provide the specific split percentages or methodology within this paper. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or cloud computing instance specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions machine learning models (e.g., Bi RNN) but does not specify version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | We train all models with 1,000 iterations and a batch size of 128 we then generate 1,000 synthetic time-series from each trained model. ... a Fourier flow (FF) model with a composition of 10 flows and a (single-layer) Bi RNN with 200 hidden units |