Uneven time series forecasting using a modified exponential smoothing method

Abstract

The article is devoted to the problem of forecasting time series with an uneven distribution of observations over time. The exponential smoothing model is used as the basic forecasting model, in which the variable weights of observations decrease exponentially. The exponential smoothing model allows us to take into account the attenuation of the correlation of cross sections of a random process of time series change over time. However, this does not take into account the factors of temporal unevenness of the results of observations and the finiteness of the sample of observations. The article describes a method for predicting an uneven time series based on a modified exponential smoothing model, in which the transition from exponential smoothing to decreasing non-exponential smoothing is carried out. The modified sequence of the weights of the observations is determined by adjusting the classically calculated exponential weights, taking into account the actual irregularity of the observations.