MUSCAT: Multi-Scale Spatio-Temporal Learning with Application to Climate Modeling
Authors: Jianpeng Xu, Xi Liu, Tyler Wilson, Pang-Ning Tan, Pouyan Hatami, Lifeng Luo
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on real-world data from the United States Historical Climate Network (USHCN) showed that MUSCAT outperformed other competing methods in more than 70% of the locations. |
| Researcher Affiliation | Academia | Jianpeng Xu1, Xi Liu1, Tyler Wilson1, Pang-Ning Tan1, Pouyan Hatami2, Lifeng Luo2, 1 Department of Computer Science and Engineering, Michigan State University 2 Department of Geography, Michigan State University {xujianpe, liuxi4, wils1270, ptan, pouyanhb, lluo}@msu.edu |
| Pseudocode | No | The paper describes algorithms using mathematical equations and textual descriptions but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it include a specific repository link or explicit code release statement. |
| Open Datasets | Yes | The climate data used in our experiments has three spatial resolutions. At the finest scale, monthly climate data are obtained for more than 300 weather stations from the United States Historical Climatology Network (USHCN)1. ... NARR2, ... NCEP reanalysis3... 1http://cdiac.ornl.gov/epubs/ndp/ushcn/ushcn.html 2https://www.esrl.noaa.gov/psd/data/gridded/data.narr.html 3http://www.esrl.noaa.gov/psd/data/gridded/data.ncep. reanalysis.derived.html |
| Dataset Splits | Yes | The 30-year climate dataset is divided into 3 partitions. We first incrementally build the models using training data from the first 10 years (1985-1994) and then apply the models to validation data from the next 10 years (1995-2004). After tuning the model hyperparameters using the validation set, we apply the chosen models to data from the last 10 years (2005-2015), which serve as our test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the general experimental setup, including data partitioning and evaluation metrics, and mentions hyperparameter tuning, but it does not provide specific values for hyperparameters (e.g., learning rate, batch size, specific regularization coefficients, iterations) or detailed training configurations. |