Comparison of Missing Data Imputation Methods in Time Series Forecasting

作     者：Hyun Ahn Kyunghee Sun Kwanghoon Pio Kim 

作者机构：Division of Software ConvergenceHanshin UniversityGyeonggi18101Korea Contents Convergence Software Research InstituteKyonggi UniversityGyeonggi16227Korea Division of AI Computer Science and EngineeringKyonggi UniversityGyeonggi16227Korea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第70卷第1期

页      面：767-779页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(Grant Number 2020R1A6A1A03040583) 

主　　题：Missing data imputation method time series forecasting LSTM 

摘      要：Time series forecasting has become an important aspect of data analysis and has many real-world ***,undesirable missing values are often encountered,which may adversely affect many forecasting *** this study,we evaluate and compare the effects of imputationmethods for estimating missing values in a time *** approach does not include a simulation to generate pseudo-missing data,but instead perform imputation on actual missing data and measure the performance of the forecasting model created *** an experiment,therefore,several time series forecasting models are trained using different training datasets prepared using each imputation ***,the performance of the imputation methods is evaluated by comparing the accuracy of the forecasting *** results obtained from a total of four experimental cases show that the k-nearest neighbor technique is the most effective in reconstructing missing data and contributes positively to time series forecasting compared with other imputation methods.