Adaptation Parameters of Time Series Models for Forecasting

Prospects for using time series to predict changes in technical parameters in real time are considered. The task is to assess the trend dynamics of the parameter. Adaptive polynomial models of the first and second order, based on the method of multiple exponential smoothing, were selected for forecasting. The models have been modified to adapt to the peculiarities of the computing process in the microcontroller. The initial data, the acceleration values in three axes, were obtained using a three-axis accelerometer installed on the vehicle. Comparison of the forecasting results showed that the second-order adaptive polynomial model is generally more preferable from the point of view of the reduced error. Both models can be used to estimate the change in a parameter for an arbitrary number of prediction intervals. The efficiency of using the models for the forecasting problem largely depends on the determination of the adaptation parameters, such as the smoothing constant and the initial estimates of the coefficients of the time series model. The paper considers the features of the behavior of the models and defines the rules for the selection of adaptation parameters depending on the nature of the change in the technical parameter over time.