Abstract
The thesis primarily deals with modifications of exponential smoothing methods for univariate time series with certain types of irregularities. A modifieed Holt method for irregular times series robust to the problem of time-close observations is suggested.
The general concept of seasonality modeling is introduced into Holt-Winters method, including a linear interpolation of seasonal indices and trigonometric functions as special cases (both applicable for irregular observations). DLS estimation of linear trend with seasonal dummies is investigated and compared with additive Holt-Winters method.
An autocorrelated term is considered as an additional component in the time series decomposition. The suggested methods are compared with the existing ones using real data examples and/or simulation study