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Forecasting the Growth of Structures from NMR and X-Ray Crystallography Experiments Released Per Year

Author

Listed:
  • Kamal Al Nasr

    (Department of Computer Science, Tennessee State University, Nashville, TN, USA2University of Texas, San Antonio, TX, USA)

  • Qasem Abu Al-Haija

    (Department of Computer and Information, Systems Engineering (CISE), Tennessee State University, Nashville, TN, USA)

Abstract

In this paper, we present a forecasting scheme for the growth of molecular structures from NMR and X-ray Crystallography experimental techniques released every year by employing an autoregressive (AR) process. The proposed scheme maximises the forecasting accuracy by utilising the optimal AR process order. The optimal model order was derived as the model with the least prediction error. Therefore, the proposed scheme has been efficiently employed to model and predict the annual growth of structures-based NMR and X-ray Crystallography experimental data for the next decade 2019–2028 using the time series of the past 43 years of both experimental datasets. The experimental results showed that the optimal model order to estimate both datasets was AR(2) which belongs to a forecasting accuracy of 98%, for both datasets. Indeed, such a high level of accuracy referred to the amount of linearity between the consecutive elements of the original times series. Hence, the forecasting results reveals of an exponential increasing behaviour in the future growth in the annual structures released from both NMR and X-ray Crystallography experiments.

Suggested Citation

  • Kamal Al Nasr & Qasem Abu Al-Haija, 2020. "Forecasting the Growth of Structures from NMR and X-Ray Crystallography Experiments Released Per Year," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-12, March.
    • Handle: RePEc:wsi:jikmxx:v:19:y:2020:i:01:n:s0219649220400043
    DOI: 10.1142/S0219649220400043
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    References listed on IDEAS

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    1. Jan Jakubík & Alena Randáková & Esam E El-Fakahany & Vladimír Doležal, 2019. "Analysis of equilibrium binding of an orthosteric tracer and two allosteric modulators," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-19, March.
    2. Luis Gonzaga Baca Ruiz & Manuel Pegalajar Cuéllar & Miguel Delgado Calvo-Flores & María Del Carmen Pegalajar Jiménez, 2016. "An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings," Energies, MDPI, vol. 9(9), pages 1-21, August.
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    Cited by:

    1. Qasem Abu Al-Haija, 2021. "A Stochastic Estimation Framework for Yearly Evolution of Worldwide Electricity Consumption," Forecasting, MDPI, vol. 3(2), pages 1-11, April.

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