International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

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DOI Prefix: 10.47001/IRJIET

Applying ARIMA Model to Predict Future Jobs in the Saudi Labor Market

Mashael AlyahyaDepartment of Information Technology, College of Computer, Qassim University, Buraydah, Saudi ArabiaMohammed HadwanDepartment of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia & Department of Computer Science, College of Applied Sciences, Taiz University, Taiz, Yemen

Vol 6 No 4 (2022): Volume 6, Issue 4, April 2022 | Pages: 1-8

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 07-04-2022

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Abstract

The labor force is one of the most critical components of advanced countries, as an investment in the labor force leads to the country's economic development. The development of the labor force and human resources requires the development of education to obtain outputs that comply with the labor market requirements. In recent years, technology has become the dominant factor in the development of countries as strength began measured by technical progress. Therefore, this paper focused on predicting future technical jobs for the KSA, especially the government sector. The used dataset is named Government job advertisements (GJA) and owned by the Ministry of Human Resources and Social Development (MHRSD). The Autoregressive Integrated Moving Average (ARIMA) was developed to obtain the results of future jobs. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE) are the elevation metrics that will be used to assess the performance of the ARIMA. Results demonstrated that the ARIMA achieves 5.45 of RMSE, 2.19 MAE, 5.44 MSE, and 4581076540077221.0 MAPE.

Keywords

ARIMA, Machine Learning, Predicting jobs, KSA, Saudi Arabia


Citation of this Article

Mashael Alyahya, Mohammed Hadwan, “Applying ARIMA Model to Predict Future Jobs in the Saudi Labor Market” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 6, Issue 4, pp 1-8, April 2022. Article DOI https://doi.org/10.47001/IRJIET/2022.604001

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. A. Adams, “Technology and the labour market: The assessment,” Oxford Review of Economic Policy, vol. 34, no. 3, pp. 349–361, 2018, doi: 10.1093/oxrep/gry010.
  2. L. Harvey, “Defining and Measuring Employability,” vol. 7, no. August 2012, pp. 37–41, 2010, doi: 10.1080/1353832012005999.
  3. J. Hillage and E. Pollard, “Developing a framework for policy analysis. Research brief, 85,” Institute for Employment Studies, no. December, 1998.
  4. T. I. Conference, L. Statisticians, T. Thirteenth, I. Conference, L. Statisticians, E. Conference, R. Iii, E. Conference, E. Conference, R. Iii, and E. Conference, “Resolution concerning statistics of the economically active population, employment, unemployment and underemployment, adopted by the Thirteenth International Conference of Labour Statisticians,” The Thirteenth International Conference of Labour Statisticians., no. i, 1982.
  5. S. A. General Authority for Statistics, “Labor Force,” Labor market statistics Q1 2021, 2021. https://www.stats.gov.sa/sites/default/files/LM_Q1 2021 %28Press release_EN%29_0.pdf (accessed May 20, 2021).
  6. R. Miller, “Jobs for the future,” Industrial Heating, vol. 74, no. 1, p. 12, 2015.
  7. N. Mezhoudi, R. Alghamdi, R. Aljunaid, G. Krichna, and D. Düştegör, “Employability prediction: a survey of current approaches, research challenges and applications,” Journal of Ambient Intelligence and Humanized Computing, 2021, doi: 10.1007/s12652-021-03276-9.
  8. “Ministry of Human Resources and Social Development (MHRSD).” https://www.bing.com/search?q=Ministry+of+Human+Resources+and+Social+Development+objectives&cvid=f0d0879e019b4b4fb90308666a26e231&aqs=edge..69i57.6745j0j1&pglt=43&FORM=ANNTA1&PC=EDGEDB (accessed Oct. 01, 2021).
  9. S. S. Alrumiah and M. Hadwan, “Implementing big data analytics in e-commerce: Vendor and customer view,” IEEE Access, vol. 9. 2021. doi: 10.1109/ACCESS.2021.3063615.
  10. N. Alajlan, M. Hadwan, and D. M. Ibrahim, “EFFECTIVENESS OF ADOPTING CLOUD-BASED E-LEARNING AT QASSIM UNIVERSITY,” Computer journal, pp. 1–9, 2020.
  11. A. Albelaihi, M. Alyahya, N. Aldieef, N. Alghasham, N. Alajlan, R. Alharbi, and M. Hadwan, “Social Commerce in Saudi Arabia: A literature review,” International Journal of Engineering Research and Technology, vol. 12, no. 12, pp. 3018–3026, 2019.
  12. S. S. Alfaozan, H. A. Alghofaily, M. A. Al-Hagery, and Mohammed A. Hadwan, “Knowledge Extraction Methods as a Measurement Tool of Depression Discovery in Saudi Society,” International Journal of Information Technology and Computer Science, vol. 12, no. 4, pp. 1–10, 2020.
  13. M. Hadwan, R. U. Khan, and K. I. M. Abuzanouneh, “Towards a smart campus for Qassim university : an investigation of Indoor navigation system .,” Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 831–837, 2019.
  14. I. Makdoun, G. Mezzour, K. M. Carley, and I. Kassou, “Analyzing the needs of the automotive job market in Morocco,” 13th International Conference on Computer Science and Education, ICCSE 2018, no. Iccse, pp. 210–215, 2018, doi: 10.1109/ICCSE.2018.8468854.
  15. S. Maheshwary and H. Misra, “Matching Resumes to Jobs via Deep Siamese Network,” The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018, no. April, pp. 87–88, 2018, doi: 10.1145/3184558.3186942.
  16. T. Ravi Kumar, P. Yasaswini, G. Rafi, and D. V. Krishna, “Comparative analysis on job prediction of students based on resume using data mining techniques,” International Journal of Engineering and Technology(UAE), vol. 7, no. 2, pp. 1100–1103, 2018, doi: 10.14419/ijet.v7i2.7.12236.
  17. C. Zhu, H. Zhu, H. Xiong, C. Ma, F. Xie, P. Ding, and P. Li, “Person-job fit: Adapting the right talent for the right job with joint representation learning,” arXiv, vol. 1, no. 1, pp. 1–16, 2018.
  18. A. S. Gnehm, “Text zoning for job advertisements with bidirectional LSTMs,” CEUR Workshop Proceedings, vol. 2226, no. June, pp. 66–74, 2018.
  19. R. Boselli, M. Cesarini, F. Mercorio, and M. Mezzanzanica, “Classifying online Job Advertisements through Machine Learning,” Future Generation Computer Systems, vol. 86, pp. 319–328, 2018, doi: 10.1016/j.future.2018.03.035.
  20. Y. Deng, H. Lei, X. Li, and Y. Lin, “An improved deep neural network model for job matching,” 2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018, pp. 106–112, 2018, doi: 10.1109/ICAIBD.2018.8396176.
  21. S. Dutta, A. Halder, and K. Dasgupta, “Design of a novel prediction engine for predicting suitable salary for a job,” Proceedings - 2018 4th IEEE International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2018, pp. 275–279, 2018, doi: 10.1109/ICRCICN.2018.8718711.
  22. V. Mathew, A. M. Chacko, and A. Udhayakumar, “Prediction of suitable human resource for replacement in skilled job positions using Supervised Machine Learning,” Proceedings of the 2018 8th International Symposium on Embedded Computing and System Design, ISED 2018, pp. 37–41, 2018, doi: 10.1109/ISED.2018.8704120.
  23. “kaggle2.”
  24. P. Pulicherla, T. Kumar, N. Abbaraju, and H. Khatri, “Job Shifting Prediction and Analysis Using Machine Learning,” Journal of Physics: Conference Series, vol. 1228, no. 1, 2019, doi: 10.1088/1742-6596/1228/1/012056.
  25. “analyticsvidhya.”
  26. H. Al-Najjar, S. S. N. Alhady, and J. M. Saleh, Improving a run time job prediction model for distributed computing based on two level predictions, vol. 547. Springer Singapore, 2019. doi: 10.1007/978-981-13-6447-1_5.
  27. Z. Hou, S. Zhao, C. Yin, Y. Wang, J. Gu, and X. Zhou, “Machine learning based performance analysis and prediction of jobs on a HPC cluster,” Proceedings - 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2019, pp. 247–252, 2019, doi: 10.1109/PDCAT46702.2019.00053.
  28. Y. Zhao, M. K. Hryniewicki, F. Cheng, B. Fu, and X. Zhu, Employee turnover prediction with machine learning: A reliable approach, vol. 869, no. January. Springer International Publishing, 2019. doi: 10.1007/978-3-030-01057-7_56.
  29. M. Alghamlas and R. Alabduljabbar, “Predicting the Suitability of IT Students’ Skills for the Recruitment in Saudi Labor Market,” 2nd International Conference on Computer Applications and Information Security, ICCAIS 2019, pp. 1–5, 2019, doi: 10.1109/CAIS.2019.8769577.
  30. “Linked in.” https://www.linkedin.com/ (accessed Jul. 03, 2021).
  31. “Bayt.” https://www.bayt.com/ (accessed May 15, 2021).
  32. M. C. M. Oo and T. Thein, “An efficient predictive analytics system for high dimensional big data,” Journal of King Saud University - Computer and Information Sciences, no. xxxx, 2019, doi: 10.1016/j.jksuci.2019.09.001.
  33. M. He, D. Shen, Y. Zhu, R. He, T. Wang, and Z. Zhang, “Career Trajectory Prediction based on CNN,” Proceedings - IEEE International Conference on Service Operations and Logistics, and Informatics 2019, SOLI 2019, pp. 22–26, 2019, doi: 10.1109/SOLI48380.2019.8955009.
  34. Y. Choi and J. W. Choi, “A study of job involvement prediction using machine learning technique,” International Journal of Organizational Analysis, vol. 29, no. 3, pp. 788–800, 2020, doi: 10.1108/IJOA-05-2020-2222.
  35. R. S. Kumar, N. Prakash, and S. Anbuchelian, “Prediction of Job Openings in IT Sector using Long Short -Term Memory Model,” Proceedings of the 4th International Conference on IoT in Social, Mobile, Analytics and Cloud, ISMAC 2020, pp. 945–953, 2020, doi: 10.1109/I-SMAC49090.2020.9243483.
  36. T. van Huynh, K. van Nguyen, N. L. T. Nguyen, and A. G. T. Nguyen, “Job prediction: From deep neural network models to applications,” 2020.
  37. V. A. Ngo, T. T. N. Doan, T. T. Le, T. H. Tran, and B. L. Do, “Exploration and Integration of Job Portals in Vietnam,” Proceedings - 2020 RIVF International Conference on Computing and Communication Technologies, RIVF 2020, 2020, doi: 10.1109/RIVF48685.2020.9140732.
  38. P. K. Roy, S. S. Chowdhary, and R. Bhatia, “A Machine Learning approach for automation of Resume Recommendation system,” Procedia Computer Science, vol. 167, no. 2019, pp. 2318–2327, 2020, doi: 10.1016/j.procs.2020.03.284.
  39. G. O. Alghamdi and A. M. Alghamdi, “Towards Building Academic Entrepreneurial Programs at Saudi Universities: Predicting Future Jobs in Light of the NEOM Project,” World Journal of Education, vol. 10, no. 4, p. 60, 2020, doi: 10.5430/wje.v10n4p60.
  40. T. Ha, M. Lee, B. Yun, and B. Y. Coh, “Job Forecasting Based on the Patent Information: A Word Embedding-Based Approach,” IEEE Access, vol. 10, pp. 7223–7233, 2022, doi: 10.1109/ACCESS.2022.3141910.
  41. “O*NET.”
  42. “cooperativepatentclassification.”
  43. G. open data for S. Government, “Government job Dataset.” data.gov.sa/Data/en/dataset (accessed Nov. 01, 2021).
  44. Ministry of Labor and Social Development, “Open Data.” https://www.data.gov.sa/Data/en/dataset?_organization_limit=0&organization=ministry_of_labor_and_social_development_-_ministry_of_planning_and_development-information_center (accessed May 03, 2021).
  45. H. H. Do, A. Broder, J. Dean, M. R. Henzinger, K. Deshmukh, and S. Sarawagi, “Data Cleaning: Problems and Current Approaches,” IEEE Data Engineering Bulletin, vol. 23, no. 4, 2000.
  46. M. Y. Mahan, C. R. Chorn, and A. P. Georgopoulos, “White Noise Test : detecting autocorrelation and nonstationarities in long time series after ARIMA modeling,” no. June 2015, 2020, doi: 10.25080/Majora-7b98e3ed-00f.
  47. “important evaultaion.”
  48. C. J. Willmott and K. Matsuura, “Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance,” Climate Research, vol. 30, no. 1, pp. 79–82, 2005, doi: 10.3354/cr030079.
  49. P. A. Thompson, “An MSE statistic for comparing forecast accuracy across series,” International Journal of Forecasting, vol. 6, no. 2, pp. 219–227, 1990, doi: 10.1016/0169-2070(90)90007-X.
  50. A. N. Koval and A. B. Severny, “On the asymmetry of the symmetric MAPE,” nternational Journal of Forecasting, vol. 4, no. 2, pp. 276–284, 1990, doi: 10.1007/BF00155226.

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