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..
Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
Authors: Yong Liu, Chenyu Li, Jianmin Wang, Mingsheng Long
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to evaluate the performance and efficiency of Koopa. For multivariate forecasting, we include six real-world benchmarks used in Autoformer [48]: ECL (UCI), ETT [53], Exchange [22], ILI (CDC), Traffic (Pe MS), and Weather (Wetterstation). For univariate forecasting, we evaluate the performance on the well-acknowledged M4 dataset [39] |
| Researcher Affiliation | Academia | Yong Liu , Chenyu Li , Jianmin Wang, Mingsheng Long B School of Software, BNRist, Tsinghua University, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Koopa Operator Adaptation. and Algorithm 2 Accelerated Koopa Operator Adaptation. |
| Open Source Code | Yes | Code is available at this repository: https://github.com/thuml/Koopa. |
| Open Datasets | Yes | For multivariate forecasting, we include six real-world benchmarks used in Autoformer [48]: ECL (UCI), ETT [53], Exchange [22], ILI (CDC), Traffic (Pe MS), and Weather (Wetterstation). For univariate forecasting, we evaluate the performance on the well-acknowledged M4 dataset [39] |
| Dataset Splits | Yes | And we follow the data processing and split ratio used in Times Net [47]. |
| Hardware Specification | Yes | Experiments are implemented in Py Torch [34] and conducted on NVIDIA TITAN RTX 24GB GPUs. |
| Software Dependencies | No | The paper mentions 'implemented in Py Torch' but does not specify the version number of PyTorch or any other key software dependencies with their versions. |
| Experiment Setup | Yes | Koopa is trained with L2 loss and optimized by ADAM [17] with an initial learning rate of 0.001 and batch size set to 32. The training process is early stopped within 10 epochs. |