A review of missing values handling methods on time-series data

Missing values becomes one of the problems that frequently occur in the data observation or data recording process. The needs of data completeness of the observation data for the uses of advanced analysis becomes important to be solved. Conventional method such as mean and mode imputation, deletion, and other methods are not good enough to handle missing values as those method can caused bias to the data. Estimation or imputation to the missing data with the values produced by some procedures or algorithms can be the best possible solution to minimized the bias effect of the conventional method of the data. So that at last, the data will be completed and ready to use for another step of analysis or data mining. In this paper, we will explain and describe several previous studies about missing values handling methods or approach on time series data. This paper also discuss some plausible option of methods to estimate missing values to be used by other researchers in this field of study. The discussion's aim is to help them to figure out what method is commonly used now along with its advantages and drawbacks.