Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Forecasting Sequential Data Using Consistent Koopman Autoencoders
Authors: Omri Azencot, N. Benjamin Erichson, Vanessa Lin, Michael Mahoney
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on a wide range of high-dimensional and short-term dependent problems, and it achieves accurate estimates for significant prediction horizons, while also being robust to noise. |
| Researcher Affiliation | Academia | 1Department of Mathematics at UC Los Angeles, CA, USA. 2ICSI and Department of Statistics at UC Berkeley, CA, USA. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at github.com/erichson/koopman AE. |
| Open Datasets | Yes | We extract a subset of the NOAA OI SST V2 High Resolution Dataset hereafter SST, and we refer to (Reynolds et al., 2007) for additional details. |
| Dataset Splits | No | The paper does not explicitly provide details about a validation dataset split. It mentions splitting data into training and test sets but omits specific validation split information. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, such as library or solver names and their exact versions. |
| Experiment Setup | Yes | Our network minimizes Eq. (13) with a decaying learning rate initially set to 0.01. We fix the loss weights to λid = λfwd = 1, λbwd = 0.1, and λcon = 0.01, for the AE, forward forecast, backward prediction and consistency, respectively. We use λs = 8 prediction steps forward and backward in time. |