IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v65y2025i3d10.1007_s10614-024-10610-8.html
   My bibliography  Save this article

Greymodels: A Shiny Package for Grey Forecasting Models in R

Author

Listed:
  • Havisha Jahajeeah

    (University of Technology)

  • Aslam A. E. F. Saib

    (University of Technology)

Abstract

The Greymodels package presents an interactive interface in R for the statistical modelling and forecasting of incomplete or small datasets using grey models. The package, based on the Shiny framework, has been designed to work with univariate and multivariate datasets having different properties and characteristics. The functionality of the package is demonstrated with a few examples and in particular, the user-friendly interface is shown to allow users to easily compare the performance of different models for prediction and among others, visualize graphical plots of predicted values within a user chosen confidence interval. The built-in algorithms in the Greymodels package are extensions or hybrids of the GM $$(1,\,1)$$ ( 1 , 1 ) model, and this article covers an overview of the theoretical background of the basic grey model and we also propose a PSO-GM $$(1,\,1)$$ ( 1 , 1 ) algorithm in this package.

Suggested Citation

  • Havisha Jahajeeah & Aslam A. E. F. Saib, 2025. "Greymodels: A Shiny Package for Grey Forecasting Models in R," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1549-1565, March.
    • Handle: RePEc:kap:compec:v:65:y:2025:i:3:d:10.1007_s10614-024-10610-8
    DOI: 10.1007/s10614-024-10610-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-024-10610-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-024-10610-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Xuemei Li & Ya Zhang & Kedong Yin, 2018. "A new grey relational model based on discrete Fourier transform and its application on Chinese marine economic," Marine Economics and Management, Emerald Group Publishing Limited, vol. 1(1), pages 79-100, July.
    2. Tuncay Özcan & Fatih Tüysüz, 2018. "Healthcare Expenditure Prediction in Turkey by Using Genetic Algorithm Based Grey Forecasting Models," International Series in Operations Research & Management Science, in: Cengiz Kahraman & Y. Ilker Topcu (ed.), Operations Research Applications in Health Care Management, chapter 0, pages 159-190, Springer.
    3. Li, Shoujun & Miao, Yanzi & Li, Guangyu & Ikram, Muhammad, 2020. "A novel varistructure grey forecasting model with speed adaptation and its application," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 172(C), pages 45-70.
    4. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "A trigonometric grey prediction approach to forecasting electricity demand," Energy, Elsevier, vol. 31(14), pages 2839-2847.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Meixia Wang, 2024. "Predicting China’s Energy Consumption and CO 2 Emissions by Employing a Novel Grey Model," Energies, MDPI, vol. 17(21), pages 1-25, October.
    2. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    3. Chia-Nan Wang & Tien-Muoi Le & Han-Khanh Nguyen, 2019. "Application of Optimization to Select Contractors to Develop Strategies and Policies for the Development of Transport Infrastructure," Mathematics, MDPI, vol. 7(1), pages 1-19, January.
    4. Jining Wang & Xuewei Zhao & Lei Wang, 2024. "Prediction of China’s Carbon Price Based on the Genetic Algorithm–Particle Swarm Optimization–Back Propagation Neural Network Model," Sustainability, MDPI, vol. 17(1), pages 1-18, December.
    5. Shubei Wang & Xiaoling Yuan & Zhongguo Jin, 2024. "Prediction of Energy-Related Carbon Emissions in East China Using a Spatial Reverse-Accumulation Discrete Grey Model," Sustainability, MDPI, vol. 16(21), pages 1-22, October.
    6. Barman, Mayur & Dev Choudhury, N.B. & Sutradhar, Suman, 2018. "A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India," Energy, Elsevier, vol. 145(C), pages 710-720.
    7. Chang, Che-Jung & Li, Der-Chiang & Huang, Yi-Hsiang & Chen, Chien-Chih, 2015. "A novel gray forecasting model based on the box plot for small manufacturing data sets," Applied Mathematics and Computation, Elsevier, vol. 265(C), pages 400-408.
    8. Wang, Qiang & Li, Shuyu & Li, Rongrong & Ma, Minglu, 2018. "Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model," Energy, Elsevier, vol. 160(C), pages 378-387.
    9. Wu, Li-Ming & Chen, Bai-Sheng & Bor, Yun-Chang & Wu, Yin-Chin, 2007. "Structure model of energy efficiency indicators and applications," Energy Policy, Elsevier, vol. 35(7), pages 3768-3777, July.
    10. Ene, Seval & Öztürk, Nursel, 2017. "Grey modelling based forecasting system for return flow of end-of-life vehicles," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 155-166.
    11. Dutta, Goutam & Mitra, Krishnendranath, 2015. "Dynamic Pricing of Electricity: A Survey of Related Research," IIMA Working Papers WP2015-08-03, Indian Institute of Management Ahmedabad, Research and Publication Department.
    12. Wang, Yuanyuan & Wang, Jianzhou & Zhao, Ge & Dong, Yao, 2012. "Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China," Energy Policy, Elsevier, vol. 48(C), pages 284-294.
    13. Weiwei Pan & Lirong Jian & Tao Liu, 2019. "Grey system theory trends from 1991 to 2018: a bibliometric analysis and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1407-1434, December.
    14. Zhao, Ze & Wang, Jianzhou & Zhao, Jing & Su, Zhongyue, 2012. "Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China," Omega, Elsevier, vol. 40(5), pages 525-532.
    15. Li-Ling Peng & Guo-Feng Fan & Min-Liang Huang & Wei-Chiang Hong, 2016. "Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting," Energies, MDPI, vol. 9(3), pages 1-20, March.
    16. Tang, Ling & Yu, Lean & Wang, Shuai & Li, Jianping & Wang, Shouyang, 2012. "A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 93(C), pages 432-443.
    17. Yi-Chung Hu, 2017. "Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting," Sustainability, MDPI, vol. 9(7), pages 1-14, July.
    18. Liu, Xiuli & Moreno, Blanca & García, Ana Salomé, 2016. "A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors," Energy, Elsevier, vol. 115(P1), pages 1042-1054.
    19. Aydin, Gokhan, 2014. "Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 382-389.
    20. OA Carboni & P. Russu, 2014. "Measuring Environmental and Economic Efficiency in Italy: an Application of the Malmquist-DEA and Grey Forecasting Model," Working Paper CRENoS 201401, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.

    More about this item

    Keywords

    Forecasting; Grey models; R software; Shiny package;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:65:y:2025:i:3:d:10.1007_s10614-024-10610-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.