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Predicting Time Series of Temperature in Nineveh Using The Conversion Function Models

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@article{IJASEIT14085,
   author = {Noor Al-Huda Mahmood Thamer and Najlaa Saad Ibrahim Alsharabi},
   title = {Predicting Time Series of Temperature in Nineveh Using  The Conversion Function Models},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {2},
   year = {2021},
   pages = {572--580},
   keywords = {Transformation function; genetic algorithm; forecasting; bleaching; TFM.},
   abstract = {

Prediction of time series is one of the topics that receive significant interest because of its importance in various fields, especially when studying natural phenomena. In this research, the transformation function model was reconciled where it aims to use the genetic algorithm to estimate the parameters of the final transformation function model.  Also, it was used to predict future values for the time series of monthly averages of temperatures in Nineveh Governorate for the period (1985-2000) as an output series and wind speed as an input series. In Nineveh Governorate, they are not stable in average and variance; when taking the square root of the data and taking the first seasonal difference as well as the first normal difference, stability was achieved, and then showed a model of the transformation function as shown in the equation (17). This research showed that the model's final parameters were estimated using the genetic algorithm based on the standard error squares average. The best estimate was chosen for the parameters that correspond to the lowest value of the average error squares, and by using this model, monthly temperature rates were predicted. Predictive values were shown to be consistent with the original values of the series. By depending on the transformation function model shown in the above equation, monthly averages of the temperature were predicted for the next four months, and the prediction results were consistent with the original time series values, which indicates the efficiency of the model.

},    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=14085},    doi = {10.18517/ijaseit.11.2.14085} }

EndNote

%A Thamer, Noor Al-Huda Mahmood
%A Alsharabi, Najlaa Saad Ibrahim
%D 2021
%T Predicting Time Series of Temperature in Nineveh Using  The Conversion Function Models
%B 2021
%9 Transformation function; genetic algorithm; forecasting; bleaching; TFM.
%! Predicting Time Series of Temperature in Nineveh Using  The Conversion Function Models
%K Transformation function; genetic algorithm; forecasting; bleaching; TFM.
%X 

Prediction of time series is one of the topics that receive significant interest because of its importance in various fields, especially when studying natural phenomena. In this research, the transformation function model was reconciled where it aims to use the genetic algorithm to estimate the parameters of the final transformation function model.  Also, it was used to predict future values for the time series of monthly averages of temperatures in Nineveh Governorate for the period (1985-2000) as an output series and wind speed as an input series. In Nineveh Governorate, they are not stable in average and variance; when taking the square root of the data and taking the first seasonal difference as well as the first normal difference, stability was achieved, and then showed a model of the transformation function as shown in the equation (17). This research showed that the model's final parameters were estimated using the genetic algorithm based on the standard error squares average. The best estimate was chosen for the parameters that correspond to the lowest value of the average error squares, and by using this model, monthly temperature rates were predicted. Predictive values were shown to be consistent with the original values of the series. By depending on the transformation function model shown in the above equation, monthly averages of the temperature were predicted for the next four months, and the prediction results were consistent with the original time series values, which indicates the efficiency of the model.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14085 %R doi:10.18517/ijaseit.11.2.14085 %J International Journal on Advanced Science, Engineering and Information Technology %V 11 %N 2 %@ 2088-5334

IEEE

Noor Al-Huda Mahmood Thamer and Najlaa Saad Ibrahim Alsharabi,"Predicting Time Series of Temperature in Nineveh Using  The Conversion Function Models," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 2, pp. 572-580, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.2.14085.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Thamer, Noor Al-Huda Mahmood
AU  - Alsharabi, Najlaa Saad Ibrahim
PY  - 2021
TI  - Predicting Time Series of Temperature in Nineveh Using  The Conversion Function Models
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 2
Y2  - 2021
SP  - 572
EP  - 580
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Transformation function; genetic algorithm; forecasting; bleaching; TFM.
N2  - 

Prediction of time series is one of the topics that receive significant interest because of its importance in various fields, especially when studying natural phenomena. In this research, the transformation function model was reconciled where it aims to use the genetic algorithm to estimate the parameters of the final transformation function model.  Also, it was used to predict future values for the time series of monthly averages of temperatures in Nineveh Governorate for the period (1985-2000) as an output series and wind speed as an input series. In Nineveh Governorate, they are not stable in average and variance; when taking the square root of the data and taking the first seasonal difference as well as the first normal difference, stability was achieved, and then showed a model of the transformation function as shown in the equation (17). This research showed that the model's final parameters were estimated using the genetic algorithm based on the standard error squares average. The best estimate was chosen for the parameters that correspond to the lowest value of the average error squares, and by using this model, monthly temperature rates were predicted. Predictive values were shown to be consistent with the original values of the series. By depending on the transformation function model shown in the above equation, monthly averages of the temperature were predicted for the next four months, and the prediction results were consistent with the original time series values, which indicates the efficiency of the model.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14085 DO - 10.18517/ijaseit.11.2.14085

RefWorks

RT Journal Article
ID 14085
A1 Thamer, Noor Al-Huda Mahmood
A1 Alsharabi, Najlaa Saad Ibrahim
T1 Predicting Time Series of Temperature in Nineveh Using  The Conversion Function Models
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 2
YR 2021
SP 572
OP 580
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Transformation function; genetic algorithm; forecasting; bleaching; TFM.
AB 

Prediction of time series is one of the topics that receive significant interest because of its importance in various fields, especially when studying natural phenomena. In this research, the transformation function model was reconciled where it aims to use the genetic algorithm to estimate the parameters of the final transformation function model.  Also, it was used to predict future values for the time series of monthly averages of temperatures in Nineveh Governorate for the period (1985-2000) as an output series and wind speed as an input series. In Nineveh Governorate, they are not stable in average and variance; when taking the square root of the data and taking the first seasonal difference as well as the first normal difference, stability was achieved, and then showed a model of the transformation function as shown in the equation (17). This research showed that the model's final parameters were estimated using the genetic algorithm based on the standard error squares average. The best estimate was chosen for the parameters that correspond to the lowest value of the average error squares, and by using this model, monthly temperature rates were predicted. Predictive values were shown to be consistent with the original values of the series. By depending on the transformation function model shown in the above equation, monthly averages of the temperature were predicted for the next four months, and the prediction results were consistent with the original time series values, which indicates the efficiency of the model.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14085 DO - 10.18517/ijaseit.11.2.14085