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README.md

Deep Learning for Time Series Classification

This is the companion repository for our paper titled "Deep learning for time series classification: a review" published in Data Mining and Knowledge Discovery, also available on ArXiv.

architecture resnet

Data

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.

Code

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.

To run a model on one dataset you should issue the following command:

python3 main.py TSC 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).

Prerequisites

All python packages needed are listed in pip-requirements.txt file and can be installed simply using the pip command.

Results

Our results 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.

Reference

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                = {ArXiv},
  Year                     = {2018}
}

Acknowledgement

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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