Machine Learning-Based Univariate Time Series Imputation Method for Estimating Missing Values in Non-Stationary Data

Abstract

Handling missing values in time series data is crucial because they can disrupt data analysis and interpretation. Sequentially missing values in time series often pose a more complex challenge compared to randomly missing values. One of the promising recent methods is Machine Learning-Based Univariate Time Series Imputation (MLBUI), although it is still not widely used and its accessibility is limited. MLBUI employs Random Forest Regression (RFR) and Support Vector Regression (SVR) algorithms. This study evaluates the performance of MLBUI in addressing missing data scenarios in non-stationary univariate time series data. The data used in this research is the average temperature data from Bogor Regency. The missing data scenarios considered include rates of 6%, 10%, and 14%. Besides MLBUI, five other comparison methods are used: Kalman StructTS, Kalman Auto-ARIMA, Spline Interpolation, Stine Interpolation, and Moving Average. The results show that MLBUI performs poorly for non-stationary data, although the obtained Mean Absolute Percentage Error (MAPE) is below 10%.