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Dr. Faridoon Khan
Assistant Professor
Contact Information
  [email protected]
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Education Detail
PhD (Econometrics) Pakistan Institute of Development Economics, Islamabad, Pakistan
MPhil (Statistics) Quaid-e-Azam University Islamabad
MSc (Statistics) University of Peshawar, Peshawar
BSc (--) Govt. Degree College Peshawar

1. Sarwar, J., Khan, S. A., Azmat, M., & Khan, F. (2024). An Application of Hybrid Bagging-Boosting Decision Trees Ensemble Model for Riverine Flood Susceptibility Mapping and Regional Risk Delineation. Water Resources Management, 1-31. (Impact Factor: 3.9) 2. Khan, F., & Albalawi, O. (2024). Analysis of Fat Big Data Using Factor Models and Penalization Techniques: A Monte Carlo Simulation and Application. Axioms, 13(7), 418. (Impact Factor: 1.9) 3. Iftikhar, H., Khan, F., Rodrigues, P. C., Alharbi, A. A., & Allohibi, J. (2025). Forecasting of Inflation Based on Univariate and Multivariate Time Series Models: An Empirical Application. Mathematics, 13(7), 1121. (Impact Factor 2.3) 4. Odhah, O. H., Alshanbari, H. M., Ahmad, Z., Khan, F., & El-Bagoury, A. A. A. H. (2024). A new family of distributions using a trigonometric function: Properties and applications in the healthcare sector. Heliyon, 10(9). 5. Sarwar, J., Khan, S. A., Azmat, M., & Khan, F. (2024). A comparative analysis of feature selection models for spatial analysis of floods using hybrid metaheuristic and machine learning models. Environmental Science and Pollution Research, 1-20. (Impact Factor: 5.8) 6. Cao, H., Khan, F. U., Shaikh, P. A., Khan, F., Zehri, M., & Yousaf, H. (2024). Does Oil Price Shock Drive Inflation? Evidence from G20 Countries. Journal of the Knowledge Economy, 1-21. (Impact Factor: 3.3) 7. Khan, F., Muhammadullah, S., Sharif, A., & Lee, C. C. (2023). The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models. Energy Economics, 107269. (Impact Factor: 12.8) 8. Gul, A., Sandhu, A. J., Farooq, M., Adil, M., Hassan, Y., & Khan, F. (2023). Half logistic-truncated exponential distribution: Characteristics and applications. Plos one, 18(11), e0285992. Impact Factor: 3.7) 9. Khan, F., Ahmad, Z., Khosa, S. K., Alomair, M. A., Alomair, A. M., & khalid Alsharidi, A. (2023). A New Modification of the Flexible Weibull Distribution Based on Power Transformation: Monte Carlo Simulation and Applications. Heliyon. (Impact Factor: 4) 10. Alshanbari, H. M., Ahmad, Z., Khan, F., Khosa, S. K., Ilyas, M., & El-Bagoury, A. A. A. H. (2023). Univariate and multivariate analyses of the asset returns using new statistical models and penalized regression techniques. AIMS Mathematics, 8(8), 19477-19503. (Impact Factor: 2.2) 11. Khan, F. U., Khan, F., & Shaikh, P. A. (2023). Forecasting returns volatility of cryptocurrency by applying various deep learning algorithms. Future Business Journal, 9(1), 25. (ESCI)12. Alshanbari, H. M., Odhah, O. H., Ahmad, Z., Khan, F., & El-Bagoury, A. A. A. H. (2023). A New Probability Distribution: Model, Theory and Analyzing the Recovery Time Data. Axioms, 12(5), 477. (Impact Factor: 2) 13. Mobeen, H., Khan, S. A., Khan, F., & Younis, N. (2022). Modeling Volatility Spillover and Role of Volatility Spillover Effect to Improve Forecasting Performance of GARCH Models Based on Varying Distributions. Kashmir Economic Review, 31(2). (HEC approved Y Category) 14. Alshanbari, H. M., Iftikhar, H., Khan, F., Rind, M., Ahmad, Z., & El-Bagoury, A. A. A. H. (2023). On the Implementation of the Artificial Neural Network Approach for Forecasting Different Healthcare Events. Diagnostics, 13(7), 1310. (Impact Factor: 3.6) 15. Iftikhar, H., Khan, M., Khan, Z., Khan, F., Alshanbari, H. M., & Ahmad, Z. (2023). A Comparative Analysis of Machine Learning Models: A Case Study in Predicting Chronic Kidney Disease. Sustainability, 15(3), 2754. (Impact Factor: 3.9) 16. Eliwa, M. S., Ahmad, Z., Alabdulhadi, M. H., & Khan, F. A New Beta Power Generator for Continuous Random Variable: Features and Inference to Model Asymmetric Data. (HEC Approved Y Category)17. Bantan, R. A., Ahmad, Z., Khan, F., Elgarhy, M., Almaspoor, Z., Hamedani, G. G., ... & Gemeay, A. M. (2023). Predictive modeling of the COVID-19 data using a new version of the flexible Weibull model and machine leaning techniques. Math. Biosci. Eng, 20, 2847-2873. (Impact Factor: 2.6) 118. Zhou, Y., Ahmad, Z., Almaspoor, Z., Khan, F., Tag-Eldin, E., Iqbal, Z., & El-Morshedy, M. (2023). On the implementation of a new version of the Weibull distribution and machine learning approach to model the COVID-19 data. Mathematical biosciences and engineering: MBE, 20(1), 337-364. (Impact Factor: 2.6) 19. Wang, Y., Ahmad, Z., Khan, F., Alnagar, D. K., Alsuhabi, H., Alkhairy, I., & Yusuf, M. (2023). Analysis of cryptocurrency exchange rates vs USA dollars using a new Dagum model. Alexandria Engineering Journal, 64, 645-658. (Impact Factor: 6.8) 20. Ahmad, Z., Almaspoor, Z., Khan, F., Alhazmi, S. E., El-Morshedy, M., Ababneh, O. Y., & Al-Omari, A. I. (2022). On fitting and forecasting the log-returns of cryptocurrency exchange rates using a new logistic model and machine learning algorithms. AI MS Math, 7, 18031-18049. (Impact Factor: 2.2)21. Wang, F., Ahmad, Z., Khan, F., Hussam, E., El-Bagoury, A. A. H., & Alshanbari, H. M. (2022). A new statistical distribution with applications to sports and health sciences. Alexandria Engineering Journal, 61(12), 9661-9671. (Impact Factor: 6.8) 22. Khan, F. U., Rafique, A., Ullah, E., & Khan, F. (2022). Revisiting the relationship between remittances and CO2 emissions by applying a novel dynamic simulated ARDL: empirical evidence from G-20 economies. Environmental Science and Pollution Research, 29(47), 71190-71207. (Impact Factor: 5.8) 23. Ahmad, Z., Almaspoor, Z., Khan, F., & El-Morshedy, M. (2022). On predictive modeling using a new flexible Weibull distribution and machine learning approach: Analyzing the COVID-19 data. Mathematics, 10(11), 1792. (Impact Factor: 2.4) 24. Ali, A., Salman, A., Amjad, F., & Khan, F. (2022). Growth Dynamics of Sulfur Emissions in the Case of SAARC Countries. Perspectives on Global Development and Technology, 21(1), 100-117. (ESCI) 25. Khan, F., Urooj, A., Khan, S. A., Khosa, S. K., Muhammadullah, S., & Almaspoor, Z. (2022). Evaluating the performance of feature selection methods using huge big data: a Monte Carlo simulation approach. Mathematical Problems in Engineering, 2022, 1-10. (Impact Factor: 1.43) 26. Khan, F., Urooj, A., Khan, S. A., Khosa, S. K., Muhammadullah, S., & Almaspoor, Z. (2022). Evaluating the performance of feature selection methods using huge big data: a Monte Carlo simulation approach. Mathematical Problems in Engineering, 2022, 1-10. (Impact Factor: 2.2) 27. Muhammadullah, S., Urooj, A., Khan, F., Alshahrani, M. N., Alqawba, M., & Al-Marzouki, S. (2022). Comparison of weighted lag adaptive LASSO with Autometrics for covariate selection and forecasting using time-series data. Complexity, 2022, 1-10. (Impact Factor: 2.3) 28. Khan, F., Urooj, A., & Muhammadullah, S. (2021). An ARIMA-ANN hybrid model for monthly gold price forecasting: empirical evidence from Pakistan. Pakistan Econ Rev, 4(1), 61-75. (HEC Approved Y Category) 29. Shah, B. A., Shareef, M., & Khan, F. (2021). Factors affecting credit risk in Islamic banks. The Discourse, 7(2), 1-15. (HEC Approved Y Category) 30. Khan, F., Urooj, A., Khan, S. A., Alsubie, A., Almaspoor, Z., & Muhammadullah, S. (2021). Comparing the forecast performance of advanced statistical and machine learning techniques using huge big data: evidence from Monte Carlo experiments. Complexity, 2021, 1-11. (Impact Factor: 2.3)31. Iftikhar, H., Khan, F., Torres Armas, E. A., Rodrigues, P. C., & López-Gonzales, J. L. (2025). A novel hybrid framework for forecasting stock indices based on the nonlinear time series models. Computational Statistics, 1-24. (Impact Factor: 1)32. Iftikhar, H., Khan, F., Rodrigues, P. C., Alharbi, A. A., & Allohibi, J. (2025). Forecasting of Inflation Based on Univariate and Multivariate Time Series Models: An Empirical Application. Mathematics, 13(7), 1121. (Impact Factor: 2.3)33. Khan, F., Urooj, A., Ullah, K., Alnssyan, B., & Almaspoor, Z. (2021). A comparison of Autometrics and penalization techniques under various error distributions: evidence from Monte Carlo simulation. Complexity, 2021, 1-8. (Impact Factor: 2.3)34. Peng, Z., Khan, F. U., Khan, F., Shaikh, P. A., Yonghong, D., Ullah, I., & Ullah, F. (2021). An application of hybrid models for weekly stock market index prediction: Empirical evidence from SAARC countries. Complexity, 2021, 1-10. (Impact Factor: 2.3)35. Muhammadullah, S., Urooj, A., & Khan, F. (2021). A revisit of the unemployment rate, interest rate, GDP growth and Inflation of Pakistan: Whether Structural break or unit root?. iRASD Journal of Economics, 3(2), 80-92. (HEC Approved Y Category)36. Rasheed, Z., Hussain, B., Shahzad, Q., & Khan, F. (2021). Role of Socio-economic Constraints in Female Education in Pakistan: A Binary Outcome Approach. Multicultural Education, 7(4). (HEC Approved Y Category)37. Khan, F., Khan, F. U., & Urooj, A. (2020). Factors Affecting Economic Growth: A Comparative Analysis of Democratic and Non-Democratic Eras of Pakistan. iRASD Journal of Economics, 2(2), 61-71. (HEC Approved Y Category)38. Ullah, I., Rehman, A., Khan, F. U., Shah, M. H., & Khan, F. (2020). Nexus between trade, CO2 emissions, renewable energy, and health expenditure in Pakistan. The International journal of health planning and management, 35(4), 818-831. (Impact Factor: 2.7)39. Khan, F. U., Ahmad, M. N., Rehman, A., Khan, F., & Khan, A. Modeling Exchange Rate Volatility in Pakistani Context The Application of Ardl Bounds Test Approach.40. Evaluating the Performance of Factor Models Against Autometrics and MachineLearning Tools Under Various Asymmetric Distributions: Simulations and Application (Under Review, Impact Factor: 2.5)41. A Novel Sparse VAR for Modelling and Forecasting Key Macroeconomic Variables of USA (Revised, Impact Factor: 2.8)42. Unmasking Return and Volatility Connectedness between Oil Market and Stock Markets of the G20 Countries (Under Review, Impact Factor: 1.9)43. An Application of Novel Machine Learning Model for Predicting the Real Estate Prices (With Editor, Impact Factor: 3.4)44. An Application of Integrated Statistical Model and Machine Learning Ensemble Model for Spatial Measurement of Land-use Transition Impacts on Hydrologic Response (Under Review, Impact Factor: 3.9)45. AI-Driven Hybrid Model for High Precision Weather Forecasting (Under Review, Impact Factor: 3.9)

Research Interest
Time Series Forecasting,
Classical and Bayesian Machine Learning,
Deep Learning for Sequential Data,
Fintech,
Energy Economics,
Climate Modeling.