Authors S. I. Klevtsov, A. B. Klevtsova
Month, Year 03, 2018 @en
Index UDC 681.3.062
Abstract When using a time series to predict the values of technical object parameters and physical variables, it is necessary to take into account the features of data extraction for forecasting associated with digital signal processing. The sampling step at data capture can be set to small. If the parameter change is insignificant for several consecutive steps, then we can use rows based on multiple exponential smoothing as the base model of the time series. To perform the prediction in the microcontroller in the background, a series with a simple implementation algorithm was chosen, but allows obtaining the result with an acceptable error. However, when predicting slowly varying physical quantities, the series has an adaptation period. The series within the adaptation period is characterized by a high error and is not suitable for forecasting. The task is to shorten the period of adaptation. This task can be solved with the help of setting the time series parameters. The paper presents the results of studies on the influence of tuning parameters on the duration and shape of the adaptation period. As parameters, the smoothing coefficient, the coefficients of the initial representation of the series, and the prediction step were considered. The effect of the series settings on the duration of the adaptation period is estimated. Based on the identified regularities, recommendations on the choice of parameters for setting the series have been determined, which will allow reducing the duration of the adaptation period. In particular, it is determined that the change in the smoothing coefficient does not significantly affect the prediction error at the initial segment. The analysis shows that in order to ensure good accuracy of forecasting with time series, it is necessary to choose a smoothing constant corresponding to the dynamics of the predicted process. To eliminate or reduce the time series adaptation and extend the prediction section, it is necessary to specify the initial values of the approximation coefficients of the original function.

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Keywords Time series; model; forecasting; technical parameter; adaptation period; microprocessor; real time.
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