Time Series Imputation by Nature and by Decomposition

Abstract

Dealing with missing time steps in time series data is a very important step in data analysis. In this paper, two new methods to impute missing time steps are presented and compared to other classical imputation methods, as well as newer, state-of-the-art methods. The first imputation method presented is Imputation by Decomposition. The second imputation method presented is Imputation by Nature. The imputation methods are used to impute a Financial Indexes and instability trackers data set, a COVID-19 data set and a Deng data set and then predictions are made and the results are presented.