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Tensorflow implementation of paper 'Learning Representations for Time Series Clustering' (NIPS 2019 accept paper). This code is not the official version.

Details

Ma, Q., Zheng, J., Li, S., & Cottrell, G. W. (2019). Learning representations for time series clustering. In Advances in neural information processing systems (pp. 3781-3791).

Bibtex

@inproceedings{ma2019learning,
  title={Learning representations for time series clustering},
  author={Ma, Qianli and Zheng, Jiawei and Li, Sen and Cottrell, Gary W},
  booktitle={Advances in neural information processing systems},
  pages={3781--3791},
  year={2019}
}

Some results

RI (Rand Index) is employed as performance (same as the paper). I used this version of the RI implementation since there is no official implementation method in sklearn package.

I run each experiment runs 5 times and report means and stand deviations. The best column represents the best performance in all the experiments. The paper column lists the RI reported by the paper.

Configs

Config1：encoder_hidden_units = [100, 50, 50], lambda = 1,

Config2：encoder_hidden_units = [100, 50, 50], lambda = 0.1,

Config3：encoder_hidden_units = [100, 50, 50], lambda = 0.01,

Config4：encoder_hidden_units = [100, 50, 50], lambda = 0.001,

Config5：encoder_hidden_units = [50, 30, 30], lambda = 1,

Config6：encoder_hidden_units = [50, 30, 30], lambda = 0.1,

Config7：encoder_hidden_units = [50, 30, 30], lambda = 0.01,

Config8：encoder_hidden_units = [50, 30, 30], lambda = 0.001.

Results

Data preprocessing method: N/A

Data preprocessing method: Normalized

Requirements

Tensorflow>=1.13.2