Learning Gaussian Mixture Representations for Tensor Time Series Forecasting
Authors: Jiewen Deng, Jinliang Deng, Renhe Jiang, Xuan Song
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | 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. |