Imputation Method Using Local Linear Regression Based on Bidirectional k-nearest-components

This paper proposes an imputation method using a bidirectional k-nearest components search based local linear regression method.The bidirectional k-nearest-components search method selects components in the dynamic range from the missing points.Unlike the existing methods, which use a fixed-size window, the proposed method can flexibly select adjacent components in an imputation problem.The weight values assigned to the components around the missing points are calculated using local linear regression.The local linear regression method is free from the rank problem in a matrix of dependent variables.In addition, it can calculate the weight values that reflect the data flow in a specific environment, such as a blackout.The original missing values were estimated from a linear combination of the components and their weights.Finally, the estimated value imputes the missing values.In the experimental results, the proposed method outperformed the existing methods when the error between the original data and imputation data was measured using MAE and RMSE.