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The workforce is an essential element in developed countries, as in vesting in it promotes the economic development of the country. To develop the labor force and human resources, it is necessary to implement an efficient educational system in order to achieve results aligned with the demands of the job market. In recent years, technology has become the main driver of national development, with power now being measured by technological advances. Thus, this study focuses on predicting future technical jobs in the United Kingdom, specifically in the field of information technology. The data used comes from ‘Glassdoor,’ a well-known platform. Glassdoor data has also been used by other sources to estimate the impact of trends and salary changes on corporate revenue. In this article, we focus on comparing ARIMA (AutoRegressive Integrated Moving Average) and SARIMA (Seasonal AutoRegressive Integrated Moving Average) models for data prediction. For this analysis, we applied the ARIMA model to obtain the results of future job trends. However, it is also relevant to compare the performance of the ARIMA model with that of the SARIMA model, which takes into account the seasonal components of the data. This can be crucial in the IT field, where demand for skills may fluctuate during specific periods of the year. Thus, this study will evaluate not only the results obtained with ARIMA but also those of SARIMA, in order to determine the most suitable model for predicting job trends in the technology sector in the United Kingdom.

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Comparison of ARIMA and SARIMA Models for Predict Ing the Evolution of IT Companies: An Analysis Based on Employee Evaluations

  • 会议论文

  • 发表日期：

    2026年

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  • Marouane Chihab,
  • Hicham Boussatta,
  • Mohamed Chiny,
  • Moulay Hadi Youssef

摘要

The workforce is an essential element in developed countries, as in vesting in it promotes the economic development of the country. To develop the labor force and human resources, it is necessary to implement an efficient educational system in order to achieve results aligned with the demands of the job market. In recent years, technology has become the main driver of national development, with power now being measured by technological advances. Thus, this study focuses on predicting future technical jobs in the United Kingdom, specifically in the field of information technology. The data used comes from ‘Glassdoor,’ a well-known platform. Glassdoor data has also been used by other sources to estimate the impact of trends and salary changes on corporate revenue. In this article, we focus on comparing ARIMA (AutoRegressive Integrated Moving Average) and SARIMA (Seasonal AutoRegressive Integrated Moving Average) models for data prediction. For this analysis, we applied the ARIMA model to obtain the results of future job trends. However, it is also relevant to compare the performance of the ARIMA model with that of the SARIMA model, which takes into account the seasonal components of the data. This can be crucial in the IT field, where demand for skills may fluctuate during specific periods of the year. Thus, this study will evaluate not only the results obtained with ARIMA but also those of SARIMA, in order to determine the most suitable model for predicting job trends in the technology sector in the United Kingdom.