Learning Representations for Time Series Clustering
Authors: Qianli Ma, Jiawei Zheng, Sen Li, Gary W. Cottrell
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on extensive time series datasets show that DTCR is state-of-the-art compared to existing methods. |
| Researcher Affiliation | Academia | Qianli Ma South China University of Technology Guangzhou, China qianlima@scut.edu.cn Jiawei Zheng South China University of Technology Guangzhou, China csjwzheng@foxmail.com Sen Li South China University of Technology Guangzhou, China awslee@foxmail.com Garrison W. Cottrell University of California, San Diego CA, USA gary@ucsd.edu |
| Pseudocode | Yes | Algorithm 1 DTCR Training Method |
| Open Source Code | No | The paper provides links to code for other methods (DTC, DEC, IDEC) in footnotes, but does not state that its own code for DTCR is open-source or provide a link for it. |
| Open Datasets | Yes | Following the protocol used in [20, 24, 5, 25, 29], we conduct experiments on the 36 UCR [30] time series datasets to evaluate performance. The statistics of these 36 datasets are shown in Table 1 of the Supplementary Material. Each data set has a default train/test split. We adopted the protocol used in USSL [29], training on the training set and evaluating on the test set for comparison. |
| Dataset Splits | No | Each data set has a default train/test split. We adopted the protocol used in USSL [29], training on the training set and evaluating on the test set for comparison. |
| Hardware Specification | Yes | The experiments are run on the Tensor Flow [32] platform using an Intel Core i7 6850K, 3.60-GHz CPU, 64-GB RAM and a Ge Force GTX 1080-Ti 11G GPU. |
| Software Dependencies | No | The paper mentions 'Tensor Flow [32] platform' and 'Adam [33] optimizer' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | In our experiments, we fixed the number of layers and the number of dilation per layer to 3 and 1, 4, and 16, respectively. The decoder is a single-layer RNN. Gated Recurrent Units (GRU) are used in the RNNs [31]. The number of units per layer of the encoder is [m1, m2, m3] {[100, 50, 50], [50, 30, 30]}. The number of hidden units in the decoder is (m1 + m2 + m3) 2. The λ of Eq. (9) {1, 1e 1, 1e 2, 1e 3}. The batch size is 2N. The Adam [33] optimizer is employed with an initial learning rate of 5e 3. |