An Estimation of Missing Value on Time Series Data Using ARMA Interpolation, Average Value and Kalman Filtration on Boeing Co Company Stock Price Data

JASDS: Journal of Applied Statistics and Data Science
Vol. 2 No. 2 (2025): Journal of Applied Statistics and Data Science

Missing data in time series data become a problem because it causes the time series to decrease. The small time series causes problems estimating model parameters, so missing data must be imputed with a value. This study aims to compare and discuss the best methods for estimating missing data, several methods that can be used to predict missing data on stock price adjustments of closed Boeing Company, which consist of the ARMA (Autoregressive Moving Average) Interpolation Method, the Kalman Filtering Method, and the Average Value Method. MAPE (Mean Absolute Percentage Error) as a measure of the goodness of the estimator to the actual value is used in determining the best imputation method among the three methods, the results of the ARMA Interpolation Method using the ARMA (1,0) time series model produce a MAPE value of 2.52%, the Kalman Filtering Method of 3.15% and Method Average Value of 5.30%. The ARMA imputation method is the best for estimating missing data on closing stock price time series data with Boeing Co adjustments, with the smallest MAPE value compared to the Kalman Filtering Method and the Average Value Method of 2.52%, which means the imputation is very good.