Time Series Classification Based on Visualization of Recurrence Plots

Lyudmyla Kirichenko and Petro Zinchenko

International Conference on Software Testing, Machine Learning and Complex Process Analysis (TMPA-2019)
7-9 November 2019, Tbilisi

Video: https://youtu.be/V5JYJmEzSog

TMPA Conference website https://tmpaconf.org/
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Transcript

1. Time series classification based on
   visualization of recurrence plots
   Lyudmyla Kirichenko, Petro Zinchenko
   Kharkiv National University of Radio Electronics, Kharkiv, Ukraine
   

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2. OUTLINE
   1. TASK OF TIME SERIES CLASSIFICATION
   2. CLASSIFICATION BY RECURRENCE PLOTS
   3. DESCRIPTION OF THE EXPERIMENT
   4. RESULTS OF CLASSIFICATION
   5. APPLICATION TO DETECT DDOS ATTACKS
   2
   

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3. TIME SERIES:CLASSIFICATION TASK
   2000 4000 6000 8000
   250
   500
   750
   1000
   1250
   1500
   Medical and biological signals Financial time series
   Network traffic
   Geophysical signals
   3
   

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4. 4
   NORMAL MACHINE LEARNING CLASSIFICATION
   

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5. 5
   The purpose of work is to conduct a comparative
   classification of noisy time series based on the visualization of
   recurrence plots
   PROPOSED MACHINE LEARNING CLASSIFICATION
   

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6. RECURRENCE PLOTS
   Time series
   Recurrence plot
   numbers of time series values
   numbers of time series values
   numbers of time series values
   

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7. RECURRENCE PLOTS
   Simple sinusoid realization
   

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8. Complex oscillating realizations
   RECURRENCE PLOTS
   

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9. RECURRENCE PLOTS
   Noise realizations
   

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10. 10
    The convolutional neural network is a class of deep learning neural
    networks. CNNs represent a huge breakthrough in image recognition.
    They are most commonly used to analyze visual imagery
    EXPERIMENT
    

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11. EXPERIMENT
    11
    Python with libraries that implement machine learning
    methods was used. The training of models for each class
    was conducted on 200 (100 and 100 for every class)
    examples of time series and tested on 50 test cases. The
    classification was performed for time series with
    different lengths, the main attention was paid to the
    series of length 256 values.
    For each time series, recurrence plots were constructed
    that were the input of the neural network.
    

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12. EXPERIMENT: NEURAL NETWORK
    12
    The ReLU non-linearity is applied to the output of every convolutional and fully-
    connected layer. For training the network was used Adam is an adaptive learning
    rate optimization algorithm
    Our convolutional neural network
    

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13. INPUT DATA: TIME SERIES
    13
    Time realization is sum of a sinusoid and noise component:
    (t) (t) z(t)
    X Y
      ,
    where Y(t) is time series, z(t) – additive noise.
    As a value characterizing the ratio of signal to noise, the coefficient Snr was used
    [Y(t)]/ [z(t)]
    Snr S S
     ,
    where S is standard deviation.
    

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14. 14
    The input time series were split into two classes. The first class consisted of sinus-
    oids, for which the frequencies varied in the range 2
    f fR
     , for the second class the
    frequency range was 2
    f fR
     . The frequency choice to the sine wave from the ranges
    1
    f fR
     and 2
    f fR
     was carried out randomly. The values 1
    f , 2
    f , fR , and
    1 2
    Fdist f f
      varied during the experiment.
    INPUT DATA: TIME SERIES
    

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15. 15
    INPUT DATA: TIME SERIES
    1st class
    2nd class
    1
    Snr  0.7
    Snr  0.4
    Snr 
    

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16. 16
    INPUT DATA: RECURRENCE PLOTS
    

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17. 17
    EXPERIMENT
    Example of part of sample recurrence plots
    

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18. 18
    Noise level Snr Accuracy Number of
    epochs
    Without noise 0.9999 8
    1 0.992 10
    0.7 0.967 14
    0.6 0.945 17
    0.5 0.778 24
    0.4 0.66 29
    EXPERIMENT: RESULTS
    

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19. 19
    2 classes: normal traffic and
    attacked traffic
    Binary classification task:
    DDoS attacks detection
    

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20. 20
    ( ) ( ) ( )*
    A A
    T t T t A t level
     
    DATA: ATTACKED AND NORMAL TRAFFICS
    Attacked traffic is sum of
    normal traffic and attack:
    

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21. 21
    RECURRENCE PLOTS of ATTACKED AND
    NORMAL TRAFFICS
    

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22. RESULTS
    22
    Attack level Accuracy
    20% 0.84
    30% 0.92
    40% 0.97
    Dependence on attack level
    

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23. • Method for classifying time series based on the construction
    of recurrence plots using the simple architecture of a
    convolutional neural network have been investigated.
    • A comparative analysis of the classification of noisy time
    series was carried out.
    • The results showed that the considered method has a fairly
    high classification accuracy even with a large degree of noise.
    • The results of the work can be used for the classification of
    time series of stochastic type by machine learning methods.
    23
    CONCLUSION
    

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24. Thanks for your attention
    24
    

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