This is the companion repository for this paper titled "Deep learning for time series classification: a review" published in Data Mining and Knowledge Discovery, also available on ArXiv.
This repository updates the code and documentation for the latest versions of tensorflow, numpy, keras, etc. The original repository is here.
The results in the paper were generated using the Tensorflow 1.14 implementation which can be found here.
The data used in this project comes from two sources:
- The UCR/UEA archive, which contains the 85 univariate time series datasets.
- The MTS archive, which contains the 13 multivariate time series datasets.
The code is divided as follows:
- The main.py python file contains the necessary code to run an experiement.
- The utils folder contains the necessary functions to read the datasets and visualize the plots.
- The classifiers folder contains nine python files one for each deep neural network tested in our paper.
The code has been testing for Tensorflow 2.16.2, numpy 1.26.4 and keras 3.4.1.
Tensorflow requires an NVIDIA GPU to run. You can verify that your pre-requisites are setup correctly by running the hello_tensorflow.py.
python3 hello_tensorflow.py
If everything is setup correctly, you should see the model training, following by the accuracy and loss.
Epoch 1/5
1875/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 1ms/step - accuracy: 0.8591 - loss: 0.4807
Epoch 2/5
1875/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9542 - loss: 0.1525
Epoch 3/5
1875/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9669 - loss: 0.1097
Epoch 4/5
1875/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9738 - loss: 0.0839
Epoch 5/5
1875/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - accuracy: 0.9768 - loss: 0.0724
313/313 - 0s - 842us/step - accuracy: 0.9793 - loss: 0.0677
To install data for testing
cd /dl-4-tsc/archives
wget https://www.cs.ucr.edu/~eamonn/time_series_data_2018/UCRArchive_2018.zip
unzip -P $password UCRArchive_2018.zip
The password can be found here.
Before running, edit main.py to point to the root of this repository on your local machine. The default
is to use relative paths and run from the top level directory of the respoitory. Otherwise, change the
path on line 83 of main.py
# change this directory for your machine
root_dir = './'
To run a model on one dataset you should issue the following command:
python3 main.py UCRArchive_2018 Coffee fcn _itr_8
which means we are launching the fcn model on the univariate UCR archive for the Coffee dataset (see constants.py for a list of possible options).
I added the results on the 128 datasets from the UCR archive 2018.
Our results in the paper showed that a deep residual network architecture performs best for the time series classification task.
The following table contains the averaged accuracy over 10 runs of each implemented model on the UCR/UEA archive, with the standard deviation between parentheses.
| Datasets | MLP | FCN | ResNet | Encoder | MCNN | t-LeNet | MCDCNN | Time-CNN | TWIESN |
|---|---|---|---|---|---|---|---|---|---|
| 50words | 68.4(7.1) | 62.7(6.1) | 74.0(1.5) | 72.3(1.0) | 22.0(24.3) | 12.5(0.0) | 58.9(5.3) | 62.1(1.0) | 49.6(2.6) |
| Adiac | 39.7(1.9) | 84.4(0.7) | 82.9(0.6) | 48.4(2.5) | 2.2(0.6) | 2.0(0.0) | 61.0(8.7) | 37.9(2.0) | 41.6(4.5) |
| ArrowHead | 77.8(1.2) | 84.3(1.5) | 84.5(1.2) | 80.4(2.9) | 33.9(4.7) | 30.3(0.0) | 68.5(6.7) | 72.3(2.6) | 65.9(9.4) |
| Beef | 72.0(2.8) | 69.7(4.0) | 75.3(4.2) | 64.3(5.0) | 20.0(0.0) | 20.0(0.0) | 56.3(7.8) | 76.3(1.1) | 53.7(14.9) |
| BeetleFly | 87.0(2.6) | 86.0(9.7) | 85.0(2.4) | 74.5(7.6) | 50.0(0.0) | 50.0(0.0) | 58.0(9.2) | 89.0(3.2) | 73.0(7.9) |
| BirdChicken | 77.5(3.5) | 95.5(3.7) | 88.5(5.3) | 66.5(5.8) | 50.0(0.0) | 50.0(0.0) | 58.0(10.3) | 60.5(9.0) | 74.0(15.6) |
| CBF | 87.2(0.7) | 99.4(0.1) | 99.5(0.3) | 94.7(1.2) | 33.2(0.1) | 33.2(0.1) | 82.0(20.5) | 95.7(1.0) | 89.0(4.9) |
| Car | 76.7(2.6) | 90.5(1.4) | 92.5(1.4) | 75.8(2.0) | 24.0(2.7) | 31.7(0.0) | 73.0(3.0) | 78.2(1.2) | 78.3(4.0) |
| ChlorineConcentration | 80.2(1.1) | 81.4(0.9) | 84.4(1.0) | 57.3(1.1) | 53.3(0.0) | 53.3(0.0) | 64.3(3.8) | 60.0(0.8) | 55.3(0.3) |
| CinC_ECG_torso | 84.0(1.0) | 82.4(1.2) | 82.6(2.4) | 91.1(2.7) | 38.1(28.0) | 25.0(0.1) | 73.6(15.2) | 74.5(4.9) | 30.0(2.9) |
| Coffee | 99.6(1.1) | 100.0(0.0) | 100.0(0.0) | 97.9(1.8) | 51.4(3.5) | 53.6(0.0) | 98.2(2.5) | 99.6(1.1) | 97.1(2.8) |
| Computers | 56.3(1.6) | 82.2(1.0) | 81.5(1.2) | 57.4(2.2) | 52.2(4.8) | 50.0(0.0) | 55.9(3.3) | 54.8(1.5) | 62.9(4.1) |
| Cricket_X | 59.1(1.1) | 79.2(0.7) | 79.1(0.6) | 69.4(1.6) | 18.9(23.8) | 7.4(0.0) | 49.5(5.3) | 55.2(2.9) | 62.2(2.1) |
| Cricket_Y | 60.0(0.8) | 78.7(1.2) | 80.3(0.8) | 67.5(1.0) | 18.4(22.0) | 8.5(0.0) | 49.7(4.3) | 57.0(2.4) | 65.6(1.3) |
| Cricket_Z | 61.7(0.8) | 81.1(1.0) | 81.2(1.4) | 69.2(1.0) | 18.3(24.4) | 6.2(0.0) | 49.8(3.6) | 48.8(2.8) | 62.2(2.3) |
| DiatomSizeReduction | 91.0(1.4) | 31.3(3.6) | 30.1(0.2) | 91.3(1.8) | 30.1(0.7) | 30.1(0.0) | 70.3(28.9) | 95.4(0.7) | 88.0(6.6) |
| DistalPhalanxOutlineAgeGroup | 65.7(1.1) | 71.0(1.3) | 71.7(1.3) | 73.7(1.6) | 46.8(0.0) | 44.6(2.3) | 74.4(2.2) | 75.2(1.4) | 71.0(2.1) |
| DistalPhalanxOutlineCorrect | 72.6(1.3) | 76.0(1.5) | 77.1(1.0) | 74.1(1.4) | 58.3(0.0) | 58.3(0.0) | 75.3(1.8) | 75.9(2.0) | 71.3(1.0) |
| DistalPhalanxTW | 61.7(1.3) | 69.0(2.1) | 66.5(1.6) | 68.8(1.6) | 30.2(0.0) | 28.3(0.7) | 67.7(1.8) | 67.3(2.8) | 60.9(3.0) |
| ECG200 | 91.6(0.7) | 88.9(1.0) | 87.4(1.9) | 92.3(1.1) | 64.0(0.0) | 64.0(0.0) | 83.3(3.9) | 81.4(1.3) | 84.2(5.1) |
| ECG5000 | 92.9(0.1) | 94.0(0.1) | 93.4(0.2) | 94.0(0.2) | 61.8(10.9) | 58.4(0.0) | 93.7(0.6) | 92.8(0.2) | 91.9(0.2) |
| ECGFiveDays | 97.0(0.5) | 98.7(0.3) | 97.5(1.9) | 98.2(0.7) | 49.9(0.3) | 49.7(0.0) | 76.2(13.4) | 88.2(1.8) | 69.8(14.1) |
| Earthquakes | 71.7(1.3) | 72.7(1.7) | 71.2(2.0) | 74.8(0.7) | 74.8(0.0) | 74.8(0.0) | 74.9(0.2) | 70.0(1.9) | 74.8(0.0) |
| ElectricDevices | 59.2(1.1) | 70.2(1.2) | 72.9(0.9) | 67.4(1.1) | 33.6(19.8) | 24.2(0.0) | 64.4(1.2) | 68.1(1.0) | 60.7(0.7) |
| FISH | 84.8(0.8) | 95.8(0.6) | 97.9(0.8) | 86.6(0.9) | 13.4(1.3) | 12.6(0.0) | 75.8(3.9) | 84.9(0.5) | 87.5(3.4) |
| FaceAll | 79.3(1.1) | 94.5(0.9) | 83.9(2.0) | 79.3(0.8) | 17.0(19.5) | 8.0(0.0) | 71.7(2.3) | 76.8(1.1) | 65.7(2.5) |
| FaceFour | 84.0(1.4) | 92.8(0.9) | 95.5(0.0) | 81.5(2.6) | 26.8(5.7) | 29.5(0.0) | 71.2(13.5) | 90.6(1.1) | 85.5(6.2) |
| FacesUCR | 83.3(0.3) | 94.6(0.2) | 95.5(0.4) | 87.4(0.4) | 15.3(2.7) | 14.3(0.0) | 75.6(5.1) | 86.9(0.7) | 64.4(2.0) |
| FordA | 73.0(0.4) | 90.4(0.2) | 92.0(0.4) | 92.3(0.3) | 51.3(0.0) | 51.0(0.8) | 79.5(2.6) | 88.1(0.7) | 52.8(2.1) |
| FordB | 60.3(0.3) | 87.8(0.6) | 91.3(0.3) | 89.0(0.5) | 49.8(1.2) | 51.2(0.0) | 53.3(2.9) | 80.6(1.5) | 50.3(1.2) |
| Gun_Point | 92.7(1.1) | 100.0(0.0) | 99.1(0.7) | 93.6(3.2) | 51.3(3.9) | 49.3(0.0) | 86.7(9.6) | 93.2(1.9) | 96.1(2.3) |
| Ham | 69.1(1.4) | 71.8(1.4) | 75.7(2.7) | 72.7(1.2) | 50.6(1.4) | 51.4(0.0) | 73.3(4.2) | 71.1(2.0) | 72.3(6.3) |
| HandOutlines | 91.8(0.5) | 80.6(7.9) | 91.1(1.4) | 89.9(2.3) | 64.1(0.0) | 64.1(0.0) | 90.9(0.6) | 88.8(1.2) | 66.0(0.7) |
| Haptics | 43.3(1.4) | 48.0(2.4) | 51.9(1.2) | 42.7(1.6) | 20.9(3.5) | 20.8(0.0) | 40.4(3.3) | 36.6(2.4) | 40.4(4.5) |
| Herring | 52.8(3.9) | 60.8(7.7) | 61.9(3.8) | 58.6(4.8) | 59.4(0.0) | 59.4(0.0) | 60.0(5.2) | 53.9(1.7) | 59.1(6.5) |
| InlineSkate | 33.7(1.0) | 33.9(0.8) | 37.3(0.9) | 29.2(0.9) | 16.7(1.6) | 16.5(1.1) | 21.5(2.2) | 28.7(1.2) | 33.0(6.8) |
| InsectWingbeatSound | 60.7(0.4) | 39.3(0.6) | 50.7(0.9) | 63.3(0.6) | 15.8(14.2) | 9.1(0.0) | 58.3(2.6) | 58.3(0.6) | 43.7(2.0) |
| ItalyPowerDemand | 95.4(0.2) | 96.1(0.3) | 96.3(0.4) | 96.5(0.5) | 50.0(0.2) | 49.9(0.0) | 95.5(1.9) | 95.5(0.4) | 88.0(2.2) |
| LargeKitchenAppliances | 47.3(0.6) | 90.2(0.4) | 90.0(0.5) | 61.9(2.6) | 41.0(16.5) | 33.3(0.0) | 43.4(2.8) | 66.6(5.0) | 77.9(1.8) |
| Lighting2 | 67.0(2.1) | 73.9(1.4) | 77.0(1.7) | 69.2(4.6) | 55.7(5.2) | 54.1(0.0) | 63.0(5.9) | 63.6(2.5) | 70.3(4.1) |
| Lighting7 | 63.0(1.7) | 82.7(2.3) | 84.5(2.0) | 62.5(2.3) | 31.0(11.3) | 26.0(0.0) | 53.4(5.9) | 65.1(3.3) | 66.4(6.6) |
| MALLAT | 91.8(0.6) | 96.7(0.9) | 97.2(0.3) | 87.6(2.0) | 13.5(3.7) | 12.3(0.1) | 90.1(5.7) | 92.0(0.7) | 59.6(9.8) |
| Meat | 89.7(1.7) | 85.3(6.9) | 96.8(2.5) | 74.2(11.0) | 33.3(0.0) | 33.3(0.0) | 70.5(8.8) | 90.2(1.8) | 96.8(2.0) |
| MedicalImages | 72.1(0.7) | 77.9(0.4) | 77.0(0.7) | 73.4(1.5) | 51.4(0.0) | 51.4(0.0) | 64.0(1.4) | 67.6(1.1) | 64.9(2.7) |
| MiddlePhalanxOutlineAgeGroup | 53.1(1.8) | 55.3(1.8) | 56.9(2.1) | 57.9(2.9) | 18.8(0.0) | 57.1(0.0) | 58.5(3.8) | 56.6(1.5) | 58.1(2.6) |
| MiddlePhalanxOutlineCorrect | 77.0(1.1) | 80.1(1.0) | 80.9(1.2) | 76.1(2.3) | 57.0(0.0) | 57.0(0.0) | 81.1(1.6) | 76.6(1.3) | 74.4(2.3) |
| MiddlePhalanxTW | 53.4(1.6) | 51.2(1.8) | 48.4(2.0) | 59.2(1.0) | 27.3(0.0) | 28.6(0.0) | 58.1(2.4) | 54.9(1.7) | 53.9(2.9) |
| MoteStrain | 85.8(0.9) | 93.7(0.5) | 92.8(0.5) | 84.0(1.0) | 50.8(4.0) | 53.9(0.0) | 76.5(14.4) | 88.2(0.9) | 78.5(4.2) |
| NonInvasiveFatalECG_Thorax1 | 91.6(0.4) | 95.6(0.3) | 94.5(0.3) | 91.6(0.4) | 16.1(29.3) | 2.9(0.0) | 90.5(1.2) | 86.5(0.5) | 49.4(4.2) |
| NonInvasiveFatalECG_Thorax2 | 91.7(0.3) | 95.3(0.3) | 94.6(0.3) | 93.2(0.9) | 16.0(29.2) | 2.9(0.0) | 91.5(1.5) | 89.8(0.3) | 52.5(3.2) |
| OSULeaf | 55.7(1.0) | 97.7(0.9) | 97.9(0.8) | 57.6(2.0) | 24.3(12.8) | 18.2(0.0) | 37.8(4.6) | 46.2(2.7) | 59.5(5.4) |
| OliveOil | 66.7(3.8) | 72.3(16.6) | 83.0(8.5) | 40.0(0.0) | 38.0(4.2) | 38.0(4.2) | 40.0(0.0) | 40.0(0.0) | 79.0(6.1) |
| PhalangesOutlinesCorrect | 73.5(2.1) | 82.0(0.5) | 83.9(1.2) | 76.7(1.4) | 61.3(0.0) | 61.3(0.0) | 80.3(1.1) | 77.1(4.7) | 65.4(0.4) |
| Phoneme | 9.6(0.3) | 32.5(0.5) | 33.4(0.7) | 17.2(0.8) | 13.2(4.0) | 11.3(0.0) | 13.0(1.0) | 9.5(0.3) | 12.8(1.4) |
| Plane | 97.8(0.5) | 100.0(0.0) | 100.0(0.0) | 97.6(0.8) | 13.0(4.5) | 13.4(1.4) | 96.5(3.2) | 96.5(1.4) | 100.0(0.0) |
| ProximalPhalanxOutlineAgeGroup | 85.6(0.5) | 83.1(1.3) | 85.3(0.8) | 84.4(1.3) | 48.8(0.0) | 48.8(0.0) | 83.8(0.8) | 82.8(1.6) | 84.4(0.5) |
| ProximalPhalanxOutlineCorrect | 73.3(1.8) | 90.3(0.7) | 92.1(0.6) | 79.1(1.8) | 68.4(0.0) | 68.4(0.0) | 87.3(1.8) | 81.2(2.6) | 82.1(0.9) |
| ProximalPhalanxTW | 76.7(0.7) | 76.7(0.9) | 78.0(1.7) | 81.2(1.1) | 35.1(0.0) | 34.6(1.0) | 79.7(1.3) | 78.3(1.2) | 78.1(0.7) |
| RefrigerationDevices | 37.9(2.1) | 50.8(1.0) | 52.5(2.5) | 48.8(1.9) | 33.3(0.0) | 33.3(0.0) | 36.9(3.8) | 43.9(1.0) | 50.1(1.5) |
| ScreenType | 40.3(1.0) | 62.5(1.6) | 62.2(1.4) | 38.3(2.2) | 34.1(2.4) | 33.3(0.0) | 42.7(1.8) | 38.9(0.9) | 43.1(4.7) |
| ShapeletSim | 50.3(3.1) | 72.4(5.6) | 77.9(15.0) | 53.0(4.7) | 50.0(0.0) | 50.0(0.0) | 50.7(4.1) | 50.0(1.3) | 61.7(10.2) |
| ShapesAll | 77.1(0.5) | 89.5(0.4) | 92.1(0.4) | 75.8(0.9) | 13.2(24.3) | 1.7(0.0) | 61.3(5.3) | 61.9(0.9) | 62.9(2.6) |
| SmallKitchenAppliances | 37.1(1.9) | 78.3(1.3) | 78.6(0.8) | 59.6(1.8) | 36.9(11.3) | 33.3(0.0) | 48.5(3.6) | 61.5(2.7) | 65.6(1.9) |
| SonyAIBORobotSurface | 67.2(1.3) | 96.0(0.7) | 95.8(1.3) | 74.3(1.9) | 44.3(4.5) | 42.9(0.0) | 65.3(10.9) | 68.7(2.3) | 63.8(9.9) |
| SonyAIBORobotSurfaceII | 83.4(0.7) | 97.9(0.5) | 97.8(0.5) | 83.9(1.0) | 59.4(7.4) | 61.7(0.0) | 77.4(6.7) | 84.1(1.7) | 69.7(4.3) |
| StarLightCurves | 94.9(0.2) | 96.1(0.9) | 97.2(0.3) | 95.7(0.5) | 65.4(16.1) | 57.7(0.0) | 93.9(1.2) | 92.6(0.2) | 85.0(0.2) |
| Strawberry | 96.1(0.5) | 97.2(0.3) | 98.1(0.4) | 94.6(0.9) | 64.3(0.0) | 64.3(0.0) | 95.6(0.6) | 95.9(0.3) | 89.5(2.0) |
| SwedishLeaf | 85.1(0.5) | 96.9(0.5) | 95.6(0.4) | 93.0(1.1) | 11.8(13.2) | 6.5(0.4) | 84.6(3.6) | 88.4(1.1) | 82.5(1.4) |
| Symbols | 83.2(1.0) | 95.5(1.0) | 90.6(2.3) | 82.1(1.9) | 22.6(16.9) | 17.4(0.0) | 75.6(11.5) | 81.0(0.7) | 75.0(8.8) |
| ToeSegmentation1 | 58.3(0.9) | 96.1(0.5) | 96.3(0.6) | 65.9(2.6) | 50.5(2.7) | 52.6(0.0) | 49.0(2.5) | 59.5(2.2) | 86.5(3.2) |
| ToeSegmentation2 | 74.5(1.9) | 88.0(3.3) | 90.6(1.7) | 79.5(2.8) | 63.2(30.9) | 81.5(0.0) | 44.3(15.2) | 73.8(2.8) | 84.2(4.6) |
| Trace | 80.7(0.7) | 100.0(0.0) | 100.0(0.0) | 96.0(1.8) | 35.4(27.7) | 24.0(0.0) | 86.3(5.4) | 95.0(2.5) | 95.9(1.9) |
| TwoLeadECG | 76.2(1.3) | 100.0(0.0) | 100.0(0.0) | 86.3(2.6) | 50.0(0.0) | 50.0(0.0) | 76.0(16.8) | 87.2(2.1) | 85.2(11.5) |
| Two_Patterns | 94.6(0.3) | 87.1(0.3) | 100.0(0.0) | 100.0(0.0) | 40.3(31.1) | 25.9(0.0) | 97.8(0.6) | 99.2(0.3) | 87.1(1.1) |
| UWaveGestureLibraryAll | 95.5(0.2) | 81.7(0.3) | 86.0(0.4) | 95.4(0.1) | 28.9(34.7) | 12.8(0.2) | 92.9(1.1) | 91.8(0.4) | 55.6(2.5) |
| Wine | 56.5(7.1) | 58.7(8.3) | 74.4(8.5) | 50.0(0.0) | 50.0(0.0) | 50.0(0.0) | 50.0(0.0) | 51.7(5.1) | 75.9(9.1) |
| WordsSynonyms | 59.8(0.8) | 56.4(1.2) | 62.2(1.5) | 61.3(0.9) | 28.4(13.6) | 21.9(0.0) | 46.3(6.1) | 56.6(0.8) | 49.0(3.0) |
| Worms | 45.7(2.4) | 76.5(2.2) | 79.1(2.5) | 57.1(3.7) | 42.9(0.0) | 42.9(0.0) | 42.6(5.5) | 38.3(2.5) | 46.6(4.5) |
| WormsTwoClass | 60.1(1.5) | 72.6(2.7) | 74.7(3.3) | 63.9(4.4) | 57.1(0.0) | 55.7(4.5) | 57.0(1.9) | 53.8(2.6) | 57.0(2.3) |
| synthetic_control | 97.6(0.4) | 98.5(0.3) | 99.8(0.2) | 99.6(0.3) | 29.8(27.8) | 16.7(0.0) | 98.3(1.2) | 99.0(0.4) | 87.4(1.6) |
| uWaveGestureLibrary_X | 76.7(0.3) | 75.4(0.4) | 78.0(0.4) | 78.6(0.4) | 18.9(21.3) | 12.5(0.4) | 71.1(1.5) | 71.1(1.1) | 60.6(1.5) |
| uWaveGestureLibrary_Y | 69.8(0.2) | 63.9(0.6) | 67.0(0.7) | 69.6(0.6) | 23.7(24.0) | 12.1(0.0) | 63.6(1.2) | 62.6(0.7) | 52.0(2.1) |
| uWaveGestureLibrary_Z | 69.7(0.2) | 72.6(0.5) | 75.0(0.4) | 71.1(0.5) | 18.0(18.4) | 12.1(0.0) | 65.0(1.8) | 64.2(0.9) | 56.5(2.0) |
| wafer | 99.6(0.0) | 99.7(0.0) | 99.9(0.1) | 99.6(0.0) | 91.3(4.4) | 89.2(0.0) | 99.2(0.3) | 96.1(0.1) | 91.4(0.5) |
| yoga | 85.5(0.4) | 83.9(0.7) | 87.0(0.9) | 82.0(0.6) | 53.6(0.0) | 53.6(0.0) | 76.2(3.9) | 78.1(0.7) | 60.7(1.9) |
| Average_Rank | 4.611765 | 2.682353 | 1.994118 | 3.682353 | 8.017647 | 8.417647 | 5.376471 | 4.970588 | 5.247059 |
| Wins | 4 | 18 | 41 | 10 | 0 | 0 | 3 | 4 | 1 |
The following table contains the averaged accuracy over 10 runs of each implemented model on the MTS archive, with the standard deviation between parentheses.
| Datasets | MLP | FCN | ResNet | Encoder | MCNN | t-LeNet | MCDCNN | Time-CNN | TWIESN |
|---|---|---|---|---|---|---|---|---|---|
| AUSLAN | 93.3(0.5) | 97.5(0.4) | 97.4(0.3) | 93.8(0.5) | 1.1(0.0) | 1.1(0.0) | 85.4(2.7) | 72.6(3.5) | 72.4(1.6) |
| ArabicDigits | 96.9(0.2) | 99.4(0.1) | 99.6(0.1) | 98.1(0.1) | 10.0(0.0) | 10.0(0.0) | 95.9(0.2) | 95.8(0.3) | 85.3(1.4) |
| CMUsubject16 | 60.0(16.9) | 100.0(0.0) | 99.7(1.1) | 98.3(2.4) | 53.1(4.4) | 51.0(5.3) | 51.4(5.0) | 97.6(1.7) | 89.3(6.8) |
| CharacterTrajectories | 96.9(0.2) | 99.0(0.1) | 99.0(0.2) | 97.1(0.2) | 5.4(0.8) | 6.7(0.0) | 93.8(1.7) | 96.0(0.8) | 92.0(1.3) |
| ECG | 74.8(16.2) | 87.2(1.2) | 86.7(1.3) | 87.2(0.8) | 67.0(0.0) | 67.0(0.0) | 50.0(17.9) | 84.1(1.7) | 73.7(2.3) |
| JapaneseVowels | 97.6(0.2) | 99.3(0.2) | 99.2(0.3) | 97.6(0.6) | 9.2(2.5) | 23.8(0.0) | 94.4(1.4) | 95.6(1.0) | 96.5(0.7) |
| KickvsPunch | 61.0(12.9) | 54.0(13.5) | 51.0(8.8) | 61.0(9.9) | 54.0(9.7) | 50.0(10.5) | 56.0(8.4) | 62.0(6.3) | 67.0(14.2) |
| Libras | 78.0(1.0) | 96.4(0.7) | 95.4(1.1) | 78.3(0.9) | 6.7(0.0) | 6.7(0.0) | 65.1(3.9) | 63.7(3.3) | 79.4(1.3) |
| NetFlow | 55.0(26.1) | 89.1(0.4) | 62.7(23.4) | 77.7(0.5) | 77.9(0.0) | 72.3(17.6) | 63.0(18.2) | 89.0(0.9) | 94.5(0.4) |
| UWave | 90.1(0.3) | 93.4(0.3) | 92.6(0.4) | 90.8(0.4) | 12.5(0.0) | 12.5(0.0) | 84.5(1.6) | 85.9(0.7) | 75.4(6.3) |
| Wafer | 89.4(0.0) | 98.2(0.5) | 98.9(0.4) | 98.6(0.2) | 89.4(0.0) | 89.4(0.0) | 65.8(38.1) | 94.8(2.1) | 94.9(0.6) |
| WalkvsRun | 70.0(15.8) | 100.0(0.0) | 100.0(0.0) | 100.0(0.0) | 75.0(0.0) | 60.0(24.2) | 45.0(25.8) | 100.0(0.0) | 94.4(9.1) |
| Average_Rank | 5.208333 | 2.000000 | 2.875000 | 3.041667 | 7.583333 | 8.000000 | 6.833333 | 4.625000 | 4.833333 |
| Wins | 0 | 5 | 3 | 0 | 0 | 0 | 0 | 0 | 2 |
These results should give an insight of deep learning for TSC therefore encouraging researchers to consider the DNNs as robust classifiers for time series data.
If you would like to generate the critical difference diagrams using Wilcoxon Signed Rank test with Holm's alpha correction, check out the cd-diagram repository.
If you re-use this work, please cite:
@article{IsmailFawaz2018deep,
Title = {Deep learning for time series classification: a review},
Author = {Ismail Fawaz, Hassan and Forestier, Germain and Weber, Jonathan and Idoumghar, Lhassane and Muller, Pierre-Alain},
journal = {Data Mining and Knowledge Discovery},
Year = {2019},
volume = {33},
number = {4},
pages = {917--963},
}
We would like to thank the providers of the UCR/UEA archive. We would also like to thank NVIDIA Corporation for the Quadro P6000 grant and the Mésocentre of Strasbourg for providing access to the cluster. We would also like to thank François Petitjean and Charlotte Pelletier for the fruitful discussions, their feedback and comments while writing this paper.
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