Data Interpretation Algorithm for Adaptive Methods of Modeling and Forecasting Time Series

Abstract

The paper considers two forms of models: seasonal and non-seasonal analogues of oscillations. The paper analyzes the basic adaptive models: Brown, Holt, and autoregression. The parameters of adaptation and layout are considered by the method of numerical estimation of parameters. The mechanism of reflection of oscillatory (seasonal or cyclic) development of the studied process through a reproduction of the scheme of moving average and the scheme of autoregression is analyzed. The paper determines the optimal value of the smoothing coefficient through adaptive polynomial models of the first and second order. Prediction using the Winters model (exponential smoothing with multiplicative seasonality and linear growth) is proposed. The paper proves that the additive model allows building a model with multiplicative seasonality and exponential tendency. The paper proves statements that allow to choose the right method for better modeling and forecasting of data.