https://publish.thescienceinsight.com/index.php/ijgs <h2>Why to publish in IJGS?</h2> <p><em>International Journal of Grey Systems</em> is the first American journal devoted to grey system theory and its application. It is published bi-annually in English in both print and online versions. IJGS publishes novel research involving Grey System Theory and related concepts. It is devoted to the international advancement of the theory and application of grey systems and uncertainty analysis. It seeks to nurture professional communication between scientists and practitioners who are interested in the theory and application of Grey System Theory. IJGS is an open access double-blind peer-reviewed journal, published by the <a href="https://thescienceinsight.com/">Science Insight</a> (USA) bi-annually. </p> Science Insight en-US International Journal of Grey Systems 2767-6412 <p>Creative Commons Non Commercial CC BY-NC: The work is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is properly attributed.</p> <p> </p> https://publish.thescienceinsight.com/index.php/ijgs/article/view/112 <p class="Abstract"><span lang="EN-US" style="font-size: 10.5pt; line-height: 115%;">To deal with the egg price forecasting in China, the grey Holt-Winters model is optimized by the Grey Wolf Algorithm. The optimized grey Holt-Winters model outperform support vector regression for forecasting the price of eggs in the four provinces of China. The seasonality of egg price is also discussed in the current study. The forecast accuracy is gauged through the Mean Absolute Percent Error and the Root Mean Square Error. The proposed model's forecasts will provide the egg producers and consumers with better long-term information.</span></p> <p class="Abstract"> </p> Jiamin Zhang Lifeng Wu Yibo Li Copyright (c) 2025 Science Insight https://creativecommons.org/licenses/by-nc/4.0 2025-12-30 2025-12-30 5 2 5 12 10.52812/ijgs.112 https://publish.thescienceinsight.com/index.php/ijgs/article/view/105 <p>The rapid expansion of digital infrastructure presents significant forecasting and strategic planning challenges for investors and corporate decision-makers. This study applies the DGM (1,1,α) grey forecasting model to project the growth of four critical variables to 2030: global network traffic, data creation, data centre supply, and data centre demand, using secondary data. The model demonstrates high in-sample accuracy, with Mean Absolute Percentage Errors (MAPE) below 1.35%. A comparative analysis with industry benchmarks reveals strong alignment, validating the model's robustness. Key findings project sustained near-exponential growth and a critical narrowing of the global supply-demand margin. The results highlight impending market tightness and provide a quantitative framework for strategic capacity planning, capital allocation, and risk mitigation in the digital infrastructure sector, directly supporting data-driven management science applications.</p> <p> </p> Dewi Shinta Mian Aziz Hussain Copyright (c) 2025 Science Insight https://creativecommons.org/licenses/by-nc/4.0 2025-12-30 2025-12-30 5 2 13 22 10.52812/ijgs.105