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

Noor Al-Huda Mahmood Thamer, Najlaa Saad Ibrahim Alsharabi

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.


Keywords


Transformation function; genetic algorithm; forecasting; bleaching; TFM.

Full Text:

PDF

References


Yako, N., Young, T. R., Cottam Jones, J. M., Hutton, C. A., Wedd, A. G., & Xiao, Z. (2017). Copper binding and redox chemistry of the Aβ16 peptide and its variants: insights into determinants of copper-dependent reactivity. Metallomics, 9(3), 278-291.â€

Alhumaima, A. S., & Abdullaev, S. M. (2020). Tigris Basin Landscapes: Sensitivity of Vegetation Index NDVI to Climate Variability Derived from Observational and Reanalysis Data. Earth Interactions, 24(7), 1-18.â€

Sharaf, H. K., Ishak, M. R., Sapuan, S. M., Yidris, N., & Fattahi, A. (2020). Experimental and numerical investigation of the mechanical behavior of full-scale wooden cross arm in the transmission towers in terms of the load-deflection test. Journal of Materials Research and Technology, 9(4), 7937-7946.â€

Taylor, J. W., McSharry, P. E., & Buizza, R. (2009). Wind power density forecasting using ensemble predictions and time series models. IEEE Transactions on Energy Conversion, 24(3), 775-782.â€

Sadaei, H. J., e Silva, P. C. D. L., Guimarães, F. G., & Lee, M. H. (2019). Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series. Energy, 175, 365-377.â€

Sharaf, H. K., Ishak, M. R., Sapuan, S. M., & Yidris, N. (2020). Conceptual design of the cross-arm for the application in the transmission towers by using TRIZ–morphological chart–ANP methods. Journal of Materials Research and Technology, 9(4), 9182-9188.â€

Liu, N., Babushkin, V., & Afshari, A. (2014). Short-term forecasting of temperature driven electricity load using time series and neural network model. Journal of Clean Energy Technologies, 2(4), 327-331.â€

Mellit, A., Menghanem, M., & Bendekhis, M. (2005, June). Artificial neural network model for prediction solar radiation data: application for sizing stand-alone photovoltaic power system. In IEEE Power Engineering Society General Meeting, 2005 (pp. 40-44). IEEE.â€

Sharaf, H. K., Salman, S., Dindarloo, M. H., Kondrashchenko, V. I., Davidyants, A. A., & Kuznetsov, S. V. (2021). The effects of the viscosity and density on the natural frequency of the cylindrical nanoshells conveying viscous fluid. The European Physical Journal Plus, 136(1), 1-19.â€

Aue, A., Norinho, D. D., & Hörmann, S. (2015). On the prediction of stationary functional time series. Journal of the American Statistical Association, 110(509), 378-392.â€

Sharaf, H. K., Salman, S., Abdulateef, M. H., Magizov, R. R., Troitskii, V. I., Mahmoud, Z. H., ... & Mohanty, H. (2021). Role of initial stored energy on hydrogen microalloying of ZrCoAl (Nb) bulk metallic glasses. Applied Physics A, 127(1), 1-7.â€

Mathew, A., Sreekumar, S., Khandelwal, S., Kaul, N., & Kumar, R. (2016). Prediction of surface temperatures for the assessment of urban heat island effect over Ahmedabad city using linear time series model. Energy and Buildings, 128, 605-616.â€

Hill, T., O'Connor, M., & Remus, W. (1996). Neural network models for time series forecasts. Management science, 42(7), 1082-1092.â€




DOI: http://dx.doi.org/10.18517/ijaseit.11.2.14085

Refbacks

  • There are currently no refbacks.


Published by INSIGHT - Indonesian Society for Knowledge and Human Development