Time-Aware Multi-Scale RNNs for Time Series Modeling
Authors: Zipeng Chen, Qianli Ma, Zhenxi Lin
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that the model outperforms state-of-the-art methods on multivariate time series classification and human motion prediction tasks. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, South China University of Technology, Guangzhou, China 2Key Laboratory of Big Data and Intelligent Robot (South China University of Technology), Ministry of Education |
| Pseudocode | No | The paper presents mathematical equations and architectural diagrams but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 2https://github.com/qianlima-lab/TAMS-RNNs |
| Open Datasets | Yes | Following Tap Net [Zhang et al., 2020], we conduct experiments on 15 data sets from the latest MTS classification archive [Bagnall et al., 2018].Human 3.6M (H3.6M) data set [Ionescu et al., 2013].we choose the FMAsmall data set [Defferrard et al., 2016] |
| Dataset Splits | Yes | We follow the standard 80/10/10% data splitting protocols to get training, validation and testing sets |
| 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 experiments. |
| Software Dependencies | No | The paper mentions software components like the Adam optimizer and dropout operation, but does not specify version numbers for any software libraries or frameworks used in the experiments. |
| Experiment Setup | Yes | For MTS classification...The number of layers of TAMS-LSTM is set to 2, the hidden state dimension is set to 256 (d = 256), and the hidden state of the final time step is used for classification. Meanwhile, the number of small hidden states is set to 4(K = 4) with the scale set {1, 2, 4, 8}. We apply the dropout operation [Srivastava et al., 2014] to the input time series X with dropout rate of 0.1. The gradient-based optimizer Adam [Kingma and Ba, 2014] is chosen, and the learning rate is set to be 0.001. |