Review of missing values procession methods in time series data

Abstract

Abstract Missing values is common problem for a lot of time series. Lack of data can be caused with human factor, technical problems non-working measuring stations and so on. Usual methods of handling missing values in time series data suppose that there are models of time series that can make predictions at period one needs to describe. To build them it’s necessary to have data of some time lapse before the period under investigation. Inside of this set of data there shouldn’t be any missing values. So, ordinary approach supposes that there’s a lot of data before the period under question. In this research it’s supposed that missing values can be situated in time series data at any time point. Thus, there’s no whole uninterrupted segment of time series that can be used to train models. Missing values in these time series must be handled first and only after that it’s possible to construct time series mathematical models and make forecasts. At this stage one can evaluate quality of constructed models and whether handled missing values fit known data.