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.
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.
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
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