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Using Multiple Regression Model and RNN for Imputing the Missing Values of PM10 Datasets

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@article{IJASEIT11236,
   author = {Moamin Amer Hasan Alsaeegh and Osamah Basheer Shukur},
   title = {Using Multiple Regression Model and RNN for Imputing the Missing Values of PM10 Datasets},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {6},
   year = {2020},
   pages = {2582--2592},
   keywords = {multiple linear regression; MLR; missing values; recurrent neural network; RNN.},
   abstract = {

The missing value in time series data is a scientific problem that should be solved by imputing these values by following some statistical techniques. This problem is more complex due to the missing values that existed in the dependent (response) variable. Particular matter (PM10) is a time series dataset used to scale air pollution as a dependent variable, while there are many types of pollutants used as independent variables. Malaysian datasets of PM10 and several climate pollutants are examined in this study. This study aims to impute the missing values for different missing rates in a dependent variable with minimum error. In this paper, the independent variables were supposed completed while the missing values have been replaced in different rates and different distributions within the dependent variable. Multiple linear regression (MLR) has been used as a traditional method to impute the different missing values of PM10. Recurrent neural network (RNN) is combined with MLR and used to impute the missing values of PM10. The results reflected that th hybrid method outperformed MLR for imputing the missing values of PM10. In conclusion, the hybrid method MLR-RNN can be used to impute the missing values of PM10 accurately compared to other traditional methods.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11236},    doi = {10.18517/ijaseit.10.6.11236} }

EndNote

%A Alsaeegh, Moamin Amer Hasan
%A Shukur, Osamah Basheer
%D 2020
%T Using Multiple Regression Model and RNN for Imputing the Missing Values of PM10 Datasets
%B 2020
%9 multiple linear regression; MLR; missing values; recurrent neural network; RNN.
%! Using Multiple Regression Model and RNN for Imputing the Missing Values of PM10 Datasets
%K multiple linear regression; MLR; missing values; recurrent neural network; RNN.
%X 

The missing value in time series data is a scientific problem that should be solved by imputing these values by following some statistical techniques. This problem is more complex due to the missing values that existed in the dependent (response) variable. Particular matter (PM10) is a time series dataset used to scale air pollution as a dependent variable, while there are many types of pollutants used as independent variables. Malaysian datasets of PM10 and several climate pollutants are examined in this study. This study aims to impute the missing values for different missing rates in a dependent variable with minimum error. In this paper, the independent variables were supposed completed while the missing values have been replaced in different rates and different distributions within the dependent variable. Multiple linear regression (MLR) has been used as a traditional method to impute the different missing values of PM10. Recurrent neural network (RNN) is combined with MLR and used to impute the missing values of PM10. The results reflected that th hybrid method outperformed MLR for imputing the missing values of PM10. In conclusion, the hybrid method MLR-RNN can be used to impute the missing values of PM10 accurately compared to other traditional methods.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11236 %R doi:10.18517/ijaseit.10.6.11236 %J International Journal on Advanced Science, Engineering and Information Technology %V 10 %N 6 %@ 2088-5334

IEEE

Moamin Amer Hasan Alsaeegh and Osamah Basheer Shukur,"Using Multiple Regression Model and RNN for Imputing the Missing Values of PM10 Datasets," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 6, pp. 2582-2592, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.6.11236.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Alsaeegh, Moamin Amer Hasan
AU  - Shukur, Osamah Basheer
PY  - 2020
TI  - Using Multiple Regression Model and RNN for Imputing the Missing Values of PM10 Datasets
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 6
Y2  - 2020
SP  - 2582
EP  - 2592
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - multiple linear regression; MLR; missing values; recurrent neural network; RNN.
N2  - 

The missing value in time series data is a scientific problem that should be solved by imputing these values by following some statistical techniques. This problem is more complex due to the missing values that existed in the dependent (response) variable. Particular matter (PM10) is a time series dataset used to scale air pollution as a dependent variable, while there are many types of pollutants used as independent variables. Malaysian datasets of PM10 and several climate pollutants are examined in this study. This study aims to impute the missing values for different missing rates in a dependent variable with minimum error. In this paper, the independent variables were supposed completed while the missing values have been replaced in different rates and different distributions within the dependent variable. Multiple linear regression (MLR) has been used as a traditional method to impute the different missing values of PM10. Recurrent neural network (RNN) is combined with MLR and used to impute the missing values of PM10. The results reflected that th hybrid method outperformed MLR for imputing the missing values of PM10. In conclusion, the hybrid method MLR-RNN can be used to impute the missing values of PM10 accurately compared to other traditional methods.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11236 DO - 10.18517/ijaseit.10.6.11236

RefWorks

RT Journal Article
ID 11236
A1 Alsaeegh, Moamin Amer Hasan
A1 Shukur, Osamah Basheer
T1 Using Multiple Regression Model and RNN for Imputing the Missing Values of PM10 Datasets
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 6
YR 2020
SP 2582
OP 2592
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 multiple linear regression; MLR; missing values; recurrent neural network; RNN.
AB 

The missing value in time series data is a scientific problem that should be solved by imputing these values by following some statistical techniques. This problem is more complex due to the missing values that existed in the dependent (response) variable. Particular matter (PM10) is a time series dataset used to scale air pollution as a dependent variable, while there are many types of pollutants used as independent variables. Malaysian datasets of PM10 and several climate pollutants are examined in this study. This study aims to impute the missing values for different missing rates in a dependent variable with minimum error. In this paper, the independent variables were supposed completed while the missing values have been replaced in different rates and different distributions within the dependent variable. Multiple linear regression (MLR) has been used as a traditional method to impute the different missing values of PM10. Recurrent neural network (RNN) is combined with MLR and used to impute the missing values of PM10. The results reflected that th hybrid method outperformed MLR for imputing the missing values of PM10. In conclusion, the hybrid method MLR-RNN can be used to impute the missing values of PM10 accurately compared to other traditional methods.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11236 DO - 10.18517/ijaseit.10.6.11236