An Effect of Window Size in Missing Value Imputation for Multivariate Time Series Data
Keywords:
Imputation, Sliding window, Multivariate dataAbstract
Insufficient or incomplete data collection cannot be used to analyze effectively. It is mandatory that the missing values must be taken care of. In the imputation process, data windowing is used for moving existing data to prediction-missing ones. This research is to study the impact of changing the data window size for missing values imputation in multivariate time series data. The research focuses on finding suitable the data window size for using to train data to imputation the missing values and import data into missing imputation values using four techniques: Row Average, K-Nearest Neighbor (KNN), Fuzzy Logic System and Artificial Neural Network (ANN). The data used in the experiment were the water level above the dam and end the dam 12 variables. The research methodology started with missing data randomization, imputation models creation, and model performance evaluation. The window sizes in the experiment were 3 days, 7 days, and 14 days past period. The results found that appropriate window size was 14 days past period. The prediction model yielded best performance of missing value imputation on test data when 14 days past period data were applied for missing value imputation
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