|Article title||SHORT-TERM FORECASTING OF TECHNICAL PARAMETER BASED ON ADAPTIVE POLYNOMIAL MODELS OF TIME SERIES|
|Authors||S.I. Klevtsov, D.А. Ivanov|
|Section||SECTION IV. METHODS, MODELS AND ALGORITHMS OF INFORMATION PROCESSING|
|Month, Year||06, 2017 @en|
|Abstract||The prospects of using time series for forecasting changes in a technical parameter in real time are considered. The task is to evaluate the dynamics of the parameter"s trend. Forecasting is carried out using simple adaptive models. This condition is associated with the implementation of the prediction procedure in the microcontroller of the object monitoring system, and the procedure should be carried out in the background. As the basic models, adaptive polynomial models of the first and second order are chosen, based on the method of multiple exponential smoothing. Models were modified to adapt to the features of the calculation process in the microcontroller. They are based on fairly simple algorithms and programs that are characterized by low computational costs and are easily implemented in the microcontroller in the background. The initial data, the accel-eration values for the three axes, were obtained using a three-axis accelerometer mounted on the car. The data before the simulation was not pre-processed. However, in the process of modeling the real-time forecasting process, emissions were excluded from the data set. The forecast was carried out by one step of the information retrieval from the sensor. The models were evaluated on the same experimental sample. A comparison of the prediction results showed that the second-order adaptive polynomial model as a whole is more preferable from the point of view of the reduced error. Both models can be used to estimate the variation of a parameter by an arbitrary number of prediction intervals. The efficiency of using models for the prediction task depends to a large extent on the definition of the adaptation parameters, such as the smoothing constant and the initial estimates of the coefficients of the time series model. In this paper, the features of model behavior are considered and the rules for selecting the adaptation parameters are determined depending on the nature of the change in the technical parameter over time.|
|Keywords||Time number; polynomial model; forecasting; technical parameter; microprocessor; real time.|
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