k-Shape is a highly accurate and efficient unsupervised method for univariate and multivariate time-series clustering. k-Shape appeared at the ACM SIGMOD 2015 conference, where it was selected as one of the (2) best papers and received the inaugural 2015 ACM SIGMOD Research Highlight Award. An extended version appeared in the ACM TODS 2017 journal. Since then, k-Shape has achieved state-of-the-art performance in both univariate and multivariate time-series datasets (i.e., k-Shape is among the fastest and most accurate time-series clustering methods, ranked in the top positions of established benchmarks with 100+ different datasets).

k-Shape has been widely adopted across different scientific areas (e.g., computer science, social science, space science, engineering, econometrics, biology, neuroscience, and medicine), Fortune 100-500 enterprises (e.g., Exelon, Nokia, and many financial firms), and organizations such as the European Space Agency.

If you use k-Shape in your project or research, cite the following two papers:

• ACM SIGMOD 2015
• ACM TODS 2017

"k-Shape: Efficient and Accurate Clustering of Time Series"
John Paparrizos and Luis Gravano
2015 ACM SIGMOD International Conference on Management of Data (ACM SIGMOD 2015)

@inproceedings{paparrizos2015k,
  title={{k-Shape: Efficient and Accurate Clustering of Time Series}},
  author={Paparrizos, John and Gravano, Luis},
  booktitle={Proceedings of the 2015 ACM SIGMOD international conference on management of data},
  pages={1855--1870},
  year={2015}
}

"Fast and Accurate Time-Series Clustering"
John Paparrizos and Luis Gravano
ACM Transactions on Database Systems (ACM TODS 2017), volume 42(2), pages 1-49

@article{paparrizos2017fast,
  title={{Fast and Accurate Time-Series Clustering}},
  author={Paparrizos, John and Gravano, Luis},
  journal={ACM Transactions on Database Systems (ACM TODS)},
  volume={42},
  number={2},
  pages={1--49},
  year={2017}
}

We thank Teja Bogireddy for his valuable help on this repository.

We also thank the initial contributors Jörg Thalheim and Gregory Rehm. The initial code was used in Sieve.

This repository contains the Python implementation for k-Shape. For the Matlab version, check here.

To ease reproducibility, we share our results over two established benchmarks:

• The UCR Univariate Archive, which contains 128 univariate time-series datasets.
  • Download all 128 preprocessed datasets here.
• The UAE Multivariate Archive, which contains 28 multivariate time-series datasets.
  • Download the first 14 preprocessed datasets here.
  • Download the remaining 14 preprocessed datasets here.

For the preprocessing steps check here.

Our code has dependencies on the following python packages:

• numpy
• pytorch

$ pip install kshape

$ git clone https://github.com/thedatumorg/kshape-python
$ cd kshape-python
$ python setup.py install

We present the runtime performance of k-Shape when varying the number of time series, number of clusters, and the lengths of time series. (All results are the average of 5 runs.)

import numpy as np
from kshape.core import kshape as ks_cpu
from kshape.core_gpu import kshape as ks_gpu

univariate_ts_datasets = np.expand_dims(np.random.rand(200, 60), axis=2)
num_clusters = 3

# CPU Model
cpu_model = ks_cpu(univariate_ts_datasets, num_clusters, centroid_init='zero', max_iter=100)

labels = np.zeros(univariate_ts_datasets.shape[0])
for i in range(num_clusters):
    labels[cpu_model[i][1]] = i
    
cluster_centroids = np.zeros((num_clusters, univariate_ts_datasets.shape[1], univariate_ts_datasets.shape[2]))
for i in range(num_clusters):
    cluster_centroids[i] = cpu_model[i][0]
    
    
# GPU Model
gpu_model = ks_gpu(univariate_ts_datasets, num_clusters, centroid_init='zero', max_iter=100)

labels = np.zeros(univariate_ts_datasets.shape[0])
for i in range(num_clusters):
    labels[gpu_model[i][1]] = i
    
cluster_centroids = np.zeros((num_clusters, univariate_ts_datasets.shape[1], univariate_ts_datasets.shape[2]))
for i in range(num_clusters):
    cluster_centroids[i] = gpu_model[i][0].detach().cpu()

import numpy as np
from kshape.core import kshape as ks_cpu
from kshape.core_gpu import kshape as ks_gpu

multivariate_ts_datasets = np.random.rand(200, 60, 6)
num_clusters = 3

# CPU Model
cpu_model = ks_cpu(multivariate_ts_datasets, num_clusters, centroid_init='zero', max_iter=100)

labels = np.zeros(multivariate_ts_datasets.shape[0])
for i in range(num_clusters):
    labels[cpu_model[i][1]] = i
    
cluster_centroids = np.zeros((num_clusters, multivariate_ts_datasets.shape[1], multivariate_ts_datasets.shape[2]))
for i in range(num_clusters):
    cluster_centroids[i] = cpu_model[i][0]
    
    
# GPU Model
gpu_model = ks_gpu(multivariate_ts_datasets, num_clusters, centroid_init='zero', max_iter=100)

labels = np.zeros(multivariate_ts_datasets.shape[0])
for i in range(num_clusters):
    labels[gpu_model[i][1]] = i
    
cluster_centroids = np.zeros((num_clusters, multivariate_ts_datasets.shape[1], multivariate_ts_datasets.shape[2]))
for i in range(num_clusters):
    cluster_centroids[i] = gpu_model[i][0].detach().cpu()

Also see Examples for UCR/UAE dataset clustering

The following tables contain the average Rand Index (RI), Adjusted Rand Index (ARI), and Normalized Mutual Information (NMI) accuracy values over 10 runs for k-Shape on the univariate and multivariate datasets.

Note: We collected the results using a single core implementation.

Server Specifications: AMD Ryzen 9 5900HX 8 Cores 3.30 GHz, 16GB RAM.

GPU Specifications: NVIDIA GeForce RTX 3070, 8GB memory.