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..
Learning Gaussian Mixture Representations for Tensor Time Series Forecasting
Authors: Jiewen Deng, Jinliang Deng, Renhe Jiang, Xuan Song
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results on two real-world TTS datasets verify the superiority of our approach compared with the state-of-the-art baselines. Code and data are published on https://github.com/beginner-sketch/GMRL. We conduct thorough experiments on two real-world datasets. |
| Researcher Affiliation | Academia | 1Southern University of Science and Technology 2University of Technology Sydney 3The University of Tokyo |
| Pseudocode | No | The paper describes the methodology textually and with mathematical equations, but does not include a distinct pseudocode block or algorithm figure. |
| Open Source Code | Yes | Code and data are published on https://github.com/beginner-sketch/GMRL. |
| Open Datasets | Yes | We conduct experiments on two real-world TTS datasets, namely NYC Traffic Demand and BJ Air Quality as listed in Table 2, the details of which are as follows: NYC Traffic Demand dataset12 is collected from NYC Bike Sharing System... BJ Air Quality dataset3 is collected from the Beijing Municipal Environmental Monitoring Center... 1https://ride.citibikenyc.com/system-data 2https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page 3https://archive.ics.uci.edu/ml/datasets/Beijing+Multi-Site+ Air-Quality+Data |
| Dataset Splits | Yes | Table 2: # Train/Val/Test 3912/240/240 |
| Hardware Specification | No | The paper mentions implementing the network with the Pytorch toolkit, but does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper states: "We implement the network with the Pytorch toolkit." However, it does not specify any version numbers for PyTorch or other software dependencies. |
| Experiment Setup | Yes | For the model, the number of GMRE-TE layers and cluster components K are set to 4 and 17. The kernel size of each dilated causal convolution component is 2, and the related expansion rate is {2, 4, 8, 16} in each GMRE-TE layer. This enables our model to handle the 16 input steps. The dimension of hidden channels dz is 24. The parameters for memory bank m and dm are set to 8 and 48. The batch size is 8, and the learning rate of the Adam optimizer is 0.0001. In addition, the inputs are normalized by Z-Score. |