Development of a combined method for predicting discrete time series with non-stability for forecasting military goods demand

The object of research is a model of the production system of military goods with non-stationary processes. In the study of the time series of the characteristics of the production system, various competing models, as a rule, are obtained under production conditions with stochastic data on the output of products due to bottleneck problems. So, the choice of the best model that describes the production system becomes difficult and critical, because some models that most closely correspond to the observed data may not foresee future values in accordance with the complexity of the model. This study seeks to demonstrate the procedure for selecting a model in a random data system using adjusted weights. This paper presents a method for combining two sets of forecasts. The obtained measurements serve as input with an autocorrelation function and a partial autocorrelation function to obtain the order of predictive models. The model parameters are evaluated and used for forecasting and compared with the original and converted data to obtain the sum of squared errors in (SSE). Models are evaluated for adequacy and subsequently tested against Akaike and Schwarz criteria. Two separate sets of forecasts of time series data are combined to form a combined set of forecasts. It should be noted that when each set of forecasts contains some independent information, combined forecasts can provide an improvement. The proposed method for combining forecasts allows to change weights, can lead to better forecasts. The main conclusion is that a set of forecasts can lead to a lower standard error than any of the initial forecasts. Past errors of each of the initial forecasts are used to determine the weight for joining two original forecasts in the formation of combined forecasts. However, the effectiveness of the forecast may change over time.