No abstract

Background. Restoring of the true regularities and missed values of time series is an important stage of data preparation for the future modeling and forecasting, therefore development of new methods of restoring is needed. Objective. To develop two-sided exponential smoothing method for restoring of regularities of dynamic processes evolution; to apply created method for restoring of missed values of London metal exchange average day prices for color metal (zinc) and to compare with methods of restoring by using arithmetic mean, autoregressive approach and exponential smoothing method. Methods. To achieve the formulated goal the following methods were used: two-sided exponential smoothing method was created; restoring by using of arithmetic mean values with usage of known values; autoregressive approach and exponential smoothing. Results. Two-sided exponential smoothing method was developed, which contains procedure of smoothing in direct and reversed time. The proposed method was used for restoring of dynamic processes and missed values of time series. Restoring of missed values of average daily prices for color metal (zinc) by making use of developed method and comparison with other methods were performed. Conclusions. It is shown by means of simulation that two-sided exponential smoothing method is effective for restoring of process regularities. Developed method for restoring missing values of zinc prices in its application on practice showed an advantage in comparison with all the methods used in this study by the values of statistical characteristics of adequacy for constructed models, so it could be used in practice. Keywords: restoring of dynamic processes regularities; restoring of missed values of time series; two-sided exponential smoothing; exponential smoothing; arithmetic mean; autoregressive approach.