pyts provides many algorithms for time series classification that have been published in the literature. Alongside high code coverage, we want to provide users confidence about our implementations of these algorithms. To do so, we created another repository where we compare the performance of several algorithms using pyts with the performance published in the original papers or on the UEA & UCR Time Series Classification Repository. We summarize the results on this page. The scripts to generate these results are notebooks that are made available on the repository.

Note: Most algorithms have hyper-parameters that need to be fine-tuned for each dataset. If the values of these hyper-parameters are not directly available, a grid search is performed using the testing set. For each of those algorithms, the accuracy reported in the pyts column is the minimum of the accuracy reported in the article and the highest accuracy obtained with the grid search (to avoid any overestimation of the performance of the algorithm because of data leakage). The same grid searches as the ones presented in the articles are usually not done for computational reasons and randomness.

UEA & UCR Time Series Classification Repository

The UEA & UCR Time Series Classification Repository is an ongoing project to develop a comprehensive repository for research into time series classification providing datasets as well as code and results for many algorithms.


The datasets used are taken from this repository. On this website, you can download the datasets (a password is required to unzip the file, you can find it by reading the PDF or the PowerPoint). Convenience functions are also provided in pyts to download a dataset from this repository:

For computational reasons, the algorithms are only tested on the smallest datasets. This way, anyone can run the scripts by themselves on a single machine and verify the results. The selected datasets are presented in the table below.

Type Name Train Test Class Length
Image Adiac 390 391 37 176
ECG ECG200 100 100 2 96
Motion GunPoint 50 150 2 150
Image MiddlePhalanxTW 399 154 6 80
Sensor Plane 105 105 7 144
Simulated SyntheticControl 300 300 6 60


1NN classifier with several metrics

The metrics used are:

  • Euclidean Distance (ED),
  • Dynamic Time Warping (DTW), and
  • Dynamic Time Warping with a learned warping window (DTW(w)).

Link to the notebook

Name ED (reported) ED (pyts) DTW (reported) DTW (pyts) DTW(w) (reported) DTW(w) (pyts)
Adiac 0.6113 0.6113 0.6036 0.6036 0.6087 0.6087
ECG200 0.8800 0.8800 0.7700 0.7700 0.8800 0.8800
GunPoint 0.9133 0.9133 0.9067 0.9067 0.9133 0.9133
MiddlePhalanxTW 0.5130 0.5130 0.5065 0.5065 0.5065 0.5065
Plane 0.9619 0.9619 1.0000 1.0000 1.0000 1.0000
SyntheticControl 0.8800 0.8800 0.9933 0.9933 0.9833 0.9833

BOSS transformer followed by a 1NN classifier using the BOSS metric

Link to the notebook

Name BOSS (reported) BOSS (pyts)
Adiac 0.765 0.752
ECG200 0.870 0.870
GunPoint 1.000 1.000
MiddlePhalanxTW 0.526 0.526
Plane 1.000 1.000
SyntheticControl 0.967 0.963

BOSSVS classifier

Link to the notebook

Name BOSSVS (reported) BOSSVS (pyts)
Adiac 0.698 0.698
ECG200 0.820 0.820
GunPoint 1.000 1.000
MiddlePhalanxTW 0.586 0.545
Plane Unreported 1.000
SyntheticControl 0.960 0.960

WEASEL transformer followed by a logistic regression

Link to the notebook

Name WEASEL (reported) WEASEL (pyts)
Adiac 0.8312 0.788
ECG200 0.8500 0.850
GunPoint 1.0000 0.960
MiddlePhalanxTW 0.5390 0.539
Plane 1.0000 1.000
SyntheticControl 0.9933 0.973

ShapeletTransform transformer followed by a Support Vector Machine with a linear kernel

Link to the notebook

Name ShapeletTransform (reported) ShapeletTransform (pyts)
Adiac 0.2379 0.238
ECG200 0.8402 0.840
GunPoint 1.0000 0.967
MiddlePhalanxTW 0.5793 0.579
Plane 1.0000 1.000
SyntheticControl 0.8733 0.873