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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

Dataset	config1	config2	config3	config4	config5	config6	config7	config8	best	paper
ArrowHead	0.63103 ± 0.04962	0.64632 ± 0.02547	0.6402 ± 0.04928	0.66869 ± 0.02821	0.6562 ± 0.0493	0.67823 ± 0.04251	0.64906 ± 0.05363	0.6529 ± 0.03482	0.74023	0.6868 ± 0.0026
Beef	0.7669 ± 0.02558	0.76644 ± 0.02347	0.77471 ± 0.02122	0.77793 ± 0.02044	0.7577 ± 0.00926	0.74897 ± 0.00958	0.75954 ± 0.01854	0.76 ± 0.01204	0.81609	0.8046 ± 0.0018
BeetleFly	0.60526 ± 0	0.61684 ± 0.02316	0.68737 ± 0.10056	0.60526 ± 0	0.60526 ± 0	0.60526 ± 0	0.63053 ± 0.05053	0.67158 ± 0.08497	0.81052	0.9000 ± 0.0001
BirdChicken	0.66211 ± 0.07688	0.58211 ± 0.08346	0.74737 ± 0.03158	0.67632 ± 0.10017	0.54737 ± 0.06781	0.57789 ± 0.10082	0.59684 ± 0.06451	0.61474 ± 0.11087	0.81053	0.8105 ± 0.0033
Car	0.64667 ± 0.03581	0.68316 ± 0.03617	0.71537 ± 0.01632	0.71797 ± 0.01905	0.6304 ± 0.02426	0.65695 ± 0.01937	0.69153 ± 0.018	0.71073 ± 0.03539	0.77401	0.75.1 ± 0.0022
ChlorineConcentration	0.52175 ± 0.01628	0.51549 ± 0.01654	0.5276 ± 0.01301	0.53374 ± 0.00277	0.5222 ± 0.01634	0.51528 ± 0.01587	0.52575 ± 0.0123	0.53555 ± 0.00072	0.53659	0.5357 ± 0.0011
Coffee	0.68624 ± 0.17581	0.65132 ± 0.12575	0.78995 ± 0.10818	0.85397 ± 0.18698	0.58942 ± 0.11309	0.60741 ± 0.04073	0.79365 ± 0.1563	0.82381 ± 0.16011	1	0.9286 ± 0.0016

Data preprocessing method: Normalized

Dataset	config1	config2	config3	config4	config5	config6	config7	config8	best	paper
ArrowHead	0.61923 ± 0.05194	0.61398 ± 0.04337	0.65328 ± 0.02648	0.66475 ± 0.03845	0.6055 ± 0.03643	0.65639 ± 0.03132	0.67137 ± 0.02044	0.66328 ± 0.03323	0.71278	0.6868 ± 0.0026
Beef	0.70713 ± 0.00892	0.70575 ± 0.00497	0.71667 ± 0.01364	0.72337 ± 0.00217	0.70851 ± 0.01202	0.72138 ± 0.01457	0.71552 ± 0.01791	0.72414 ± 0.00291	0.74483	0.8046 ± 0.0018
BeetleFly	0.71842 ± 0.16428	0.67105 ± 0.08392	0.73509 ± 0.06021	0.74211 ± 0.09676	0.62842 ± 0.02836	0.75789 ± 0.10771	0.66421 ± 0.11789	0.74211 ± 0.10458	1	0.9000 ± 0.0001
BirdChicken	0.53579 ± 0.05702	0.58596 ± 0.05674	0.64511 ± 0.09452	0.67193 ± 0.08203	0.50877 ± 0.02796	0.56632 ± 0.09342	0.65 ± 0.10556	0.64868 ± 0.02507	0.81053	0.8105 ± 0.0033
Car	0.70927 ± 0.01742	0.71119 ± 0.02797	0.72249 ± 0.02928	0.7096 ± 0.02585	0.69085 ± 0.01935	0.70395 ± 0.01768	0.71073 ± 0.03181	0.71921 ± 0.01226	0.77288	0.75.1 ± 0.0022
ChlorineConcentration	0.50288 ± 0.00019	0.50821 ± 0.01159	0.51451 ± 0.0144	0.53447 ± 0.00096	0.50255 ± 0.00008	0.5083 ± 0.01156	0.51469 ± 0.01472	0.53519 ± 0.00106	0.053889	0.5357 ± 0.0011

Requirements

Tensorflow>=1.13.2