Date Received: 20-07-2020 / Date Accepted: 10-09-2020
Time series with missing values occur in almost areas of applied science. Ignoring missing values can lead to a reduction of system performance and unreliable results, especially in case of large missing values. Therefore, handling missing data is an important task to effectively perform further purposes such as classification, data analysis, etc. This article aims first to introduce approaches for dealing with missing data. Next a framework is built to fill the incomplete data in univariate time series and then to compare the performance of various imputation methods. Four indices are used to evaluate the ability of imputation methods on 3 different real-time data series. Through experimental results, the DTWBI and eDTWBI methods achieve better results with data having seasonality component and without trend factor, while na.interp is more superior as the data have both seasonality and trend components.
