Temporal-Frequency Co-training for Time Series Semi-supervised Learning
Authors: Zhen Liu, Qianli Ma, Peitian Ma, Linghao Wang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on 106 UCR datasets show that TSTFC outperforms state-of-the-art methods, demonstrating the effectiveness and robustness of our proposed model. |
| 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 | Yes | For details of TS-TFC training, please refer to Algorithm 1 in the Appendix. |
| Open Source Code | Yes | Our implementation of TS-TFC is available at https://github.com/qianlima-lab/TS-TFC. |
| Open Datasets | Yes | We conduct experiments utilizing the UCR time series archive (Dau et al. 2019), which is widely employed for time series classification studies (Ismail Fawaz et al. 2019). |
| Dataset Splits | Yes | As suggested by (Dau et al. 2019; Wang et al. 2019), we merge the original training and test sets, and then divide the train-validation-test set using a five-fold cross-validation method in the ratio of 60%-20%-20% for evaluation. |
| Hardware Specification | Yes | All experiments are repeated five times with five random seeds, and are conducted on Pytoch 1.10 platform with 2 NVIDIA Ge Force RTX 3090 GPUs. |
| Software Dependencies | Yes | All experiments are repeated five times with five random seeds, and are conducted on Pytoch 1.10 platform with 2 NVIDIA Ge Force RTX 3090 GPUs. |
| Experiment Setup | Yes | Adam is used as the optimizer, and the learning rate is 0.001. The maximum batch size is 1024, and the maximum epoch is 1000. The temperature coefficients τ in Eq. 2 and Eq. 3 are set to 50, the hyperparameters α is set to 0.99 and 5. And top k in Eq. 4 for temporal and frequency encoder are set to 40 and 30, respectively. The fixed threshold γ is set to 0.95. The hyperparameters λ and µ are set to 0.05. Further, we employ labeled data for the warm-up training in the first 300 epochs, mitigating the learning bias of the model for unlabeled data. |