Comparison of Missing Data Imputation Methods in Time Series Forecasting

Time series forecasting has become an important aspect of data analysis and has many real-world applications.However,undesirable missing values are often encountered,which may adversely affect many forecasting tasks.In this study,we evaluate and compare the effects of imputationmethods for estimating missing values in a time series.Our 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 therefrom.In an experiment,therefore,several time series forecasting models are trained using different training datasets prepared using each imputation method.Subsequently,the performance of the imputation methods is evaluated by comparing the accuracy of the forecasting models.The 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.

The Ability of Different Imputation Methods to Preserve the Significant Genes and Pathways in Cancer

Deciphering important genes and pathways from incomplete gene expression data could facilitate a better understanding of cancer. Different imputation methods can be applied to estimate the missing values. In our study, we evaluated various imputation methods for their performance in preserving significant genes and pathways. In the first step, 5% genes are considered in random for two types of ignorable and non-ignorable missingness mechanisms with various missing rates. Next, 10 well-known imputation methods were applied to the complete datasets. The significance analysis of microarrays （SAM） method was applied to detect the significant genes in rectal and lung cancers to showcase the utility of imputation approaches in preserving significant genes. To determine the impact of different imputation methods on the identification of important genes, the chi-squared test was used to compare the proportions of overlaps between significant genes detected from original data and those detected from the imputed datasets. Additionally, the significant genes are tested for their enrichment in important pathways, using the ConsensusPathDB. Our results showed that almost all the significant genes and pathways of the original dataset can be detected in all imputed datasets, indicating that there is no significant difference in the performance of various imputation methods tested. The source code and selected datasets are available on http：//profiles.bs.ipm.ir/soft- wares/imputationmethods/.

Comparative Variance and Multiple Imputation Used for Missing Values in Land Price DataSet

Based on the two-dimensional relation table,this paper studies the missing values in the sample data of land price of Shunde District of Foshan City.GeoDa software was used to eliminate the insignificant factors by stepwise regression analysis;NORM software was adopted to construct the multiple imputation models;EM algorithm and the augmentation algorithm were applied to fit multiple linear regression equations to construct five different filling datasets.Statistical analysis is performed on the imputation data set in order to calculate the mean and variance of each data set,and the weight is determined according to the differences.Finally,comprehensive integration is implemented to achieve the imputation expression of missing values.The results showed that in the three missing cases where the PRICE variable was missing and the deletion rate was 5%,the PRICE variable was missing and the deletion rate was 10%,and the PRICE variable and the CBD variable were both missing.The new method compared to the traditional multiple filling methods of true value closer ratio is 75%to 25%,62.5%to 37.5%,100%to 0%.Therefore,the new method is obviously better than the traditional multiple imputation methods,and the missing value data estimated by the new method bears certain reference value.

HIOC:a hybrid imputation method to predict missing values in medical datasets

Purpose-Decision support systems developed using machine learning classifiers have become a valuable tool in predicting various diseases.However,the performance of these systems is adversely affected by the missing values in medical datasets.Imputation methods are used to predict these missing values.In this paper,a new imputation method called hybrid imputation optimized by the classifier(HIOC)is proposed to predict missing values efficiently.Design/methodology/approach-The proposed HIOC is developed by using a classifier to combine multivariate imputation by chained equations(MICE),K nearest neighbor(KNN),mean and mode imputation methods in an optimum way.Performance of HIOC has been compared to MICE,KNN,and mean and mode methods.Four classifiers support vector machine(SVM),naive Bayes(NB),random forest(RF)and decision tree(DT)have been used to evaluate the performance of imputation methods.Findings-The results show that HIOC performed efficiently even with a high rate of missing values.It had reduced root mean square error(RMSE)up to 17.32%in the heart disease dataset and 34.73%in the breast cancer dataset.Correct prediction of missing values improved the accuracy of the classifiers in predicting diseases.It increased classification accuracy up to 18.61%in the heart disease dataset and 6.20%in the breast cancer dataset.Originality/value-The proposed HIOC is a new hybrid imputation method that can efficiently predict missing values in any medical dataset.