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..
Time-Aware Multi-Scale RNNs for Time Series Modeling
Authors: Zipeng Chen, Qianli Ma, Zhenxi Lin
IJCAI 2021 | Venue PDF | 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. |