IDEAS home Printed from https://ideas.repec.org/a/ist/ibsibr/v50y2021i1p15-46.html

Comparison of Forecasting Performance of ARIMA LSTM and HYBRID Models for The Sales Volume Budget of a Manufacturing Enterprise

Author

Listed:
  • Ayşe Soy Temür

    (Duzce University, Kaynaşlı Vocational School, Foreign Trade Department, Duzce, Turkey)

  • Şule Yıldız

    (Sakarya University, Faculty of Business, Department of Business Administration, Sakarya, Turkey)

Abstract

This study aims to create a monthly sales quantity budget by making use of the previous income data of an enterprise operating within the construction sector, which is considered the locomotive of the economy. For estimating time-series of sales as a linear model ARIMA (Auto-Regressive Integrated Moving Average), as nonlinear model LSTM (Long Shorterm Memory) and a HYBRID (LSTM and ARIMA) model built to improve system performance compared to a single model was used. As a result of the study, Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) values obtained from each of the methods used in the application were compared, and a monthly sales volume budget was created for 2017 with all the methods used. When the MAPE and MSE values obtained from each of these methods were compared, the best performance was the Hybrid model that gave the lowest error, and in addition, the fact that all of the application models got very realistic results by using the historical data showed the success of the predictions.

Suggested Citation

  • Ayşe Soy Temür & Şule Yıldız, 2021. "Comparison of Forecasting Performance of ARIMA LSTM and HYBRID Models for The Sales Volume Budget of a Manufacturing Enterprise," Istanbul Business Research, Istanbul University Business School, vol. 50(1), pages 15-46, May.
    • Handle: RePEc:ist:ibsibr:v:50:y:2021:i:1:p:15-46
    DOI: 10.26650/ibr.2021.51.0117
    as

    Download full text from publisher

    File URL: https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/1538A115715044948E7A508C24D08BB4
    Download Restriction: no
    File URL: https://iupress.istanbul.edu.tr/en/journal/ibr/article/comparison-of-forecasting-performance-of-arima-lstm-and-hybrid-models-for-the-sales-volume-budget-of-a-manufacturing-enterprise
    Download Restriction: no
    File URL: https://libkey.io/10.26650/ibr.2021.51.0117?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
    ---><---

    References listed on IDEAS

    as
    1. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    2. Papagera, A. & Ioannou, K. & Zaimes, G. & Iakovoglou, V. & Simeonidou, M., . "Simulation and Prediction of Water Allocation Using Artificial Neural Networks and a Spatially Distributed Hydrological Model," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 6(4), pages 1-11.
    3. Bhardwaj, Shivam & Gadre, Vikram M. & Chandrasekhar, E., 2020. "Statistical analysis of DWT coefficients of fGn processes using ARFIMA(p,d,q) models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    4. Lynn Wu & Erik Brynjolfsson, 2015. "The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales," NBER Chapters, in: Economic Analysis of the Digital Economy, pages 89-118, National Bureau of Economic Research, Inc.
    5. Koutroumanidis, Theodoros & Ioannou, Konstantinos & Arabatzis, Garyfallos, 2009. "Predicting fuelwood prices in Greece with the use of ARIMA models, artificial neural networks and a hybrid ARIMA-ANN model," Energy Policy, Elsevier, vol. 37(9), pages 3627-3634, September.
    6. Luxhoj, James T. & Riis, Jens O. & Stensballe, Brian, 1996. "A hybrid econometric--neural network modeling approach for sales forecasting," International Journal of Production Economics, Elsevier, vol. 43(2-3), pages 175-192, June.
    7. Zhou, Zhi-Jie & Hu, Chang-Hua, 2008. "An effective hybrid approach based on grey and ARMA for forecasting gyro drift," Chaos, Solitons & Fractals, Elsevier, vol. 35(3), pages 525-529.
    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. Daniya Tlegenova, 2015. "Forecasting Exchange Rates Using Time Series Analysis: The sample of the currency of Kazakhstan," Papers 1508.07534, arXiv.org.
    2. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
    3. Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
    4. Reham Alhindawi & Yousef Abu Nahleh & Arun Kumar & Nirajan Shiwakoti, 2020. "Projection of Greenhouse Gas Emissions for the Road Transport Sector Based on Multivariate Regression and the Double Exponential Smoothing Model," Sustainability, MDPI, vol. 12(21), pages 1-18, November.
    5. Soraya SEDKAOUI & Rafika Benaichouba, 2019. "How data analytics drive sharing economy business models?," Proceedings of International Academic Conferences 9911754, International Institute of Social and Economic Sciences.
    6. Ming, Yaxin & Deng, Huixin & Wu, Xiaoyue, 2022. "The negative effect of air pollution on people's pro-environmental behavior," Journal of Business Research, Elsevier, vol. 142(C), pages 72-87.
    7. Pao, H.T., 2009. "Forecasting energy consumption in Taiwan using hybrid nonlinear models," Energy, Elsevier, vol. 34(10), pages 1438-1446.
    8. Katarzyna Chudy-Laskowska & Tomasz Pisula, 2022. "An Analysis of the Use of Energy from Conventional Fossil Fuels and Green Renewable Energy in the Context of the European Union’s Planned Energy Transformation," Energies, MDPI, vol. 15(19), pages 1-23, October.
    9. Yu, Shiwei & Wei, Yi-Ming & Wang, Ke, 2012. "A PSO–GA optimal model to estimate primary energy demand of China," Energy Policy, Elsevier, vol. 42(C), pages 329-340.
    10. Kristiansen, Tarjei, 2012. "Forecasting Nord Pool day-ahead prices with an autoregressive model," Energy Policy, Elsevier, vol. 49(C), pages 328-332.
    11. Meng, Ming & Niu, Dongxiao, 2011. "Modeling CO2 emissions from fossil fuel combustion using the logistic equation," Energy, Elsevier, vol. 36(5), pages 3355-3359.
    12. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
    13. Méndez-Gordillo, Alma Rosa & Cadenas, Erasmo, 2021. "Wind speed forecasting by the extraction of the multifractal patterns of time series through the multiplicative cascade technique," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    14. Alameer, Zakaria & Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ye, Haiwang & Jianhua, Zhang, 2019. "Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm," Resources Policy, Elsevier, vol. 61(C), pages 250-260.
    15. Palanisamy Manigandan & MD Shabbir Alam & Majed Alharthi & Uzma Khan & Kuppusamy Alagirisamy & Duraisamy Pachiyappan & Abdul Rehman, 2021. "Forecasting Natural Gas Production and Consumption in United States-Evidence from SARIMA and SARIMAX Models," Energies, MDPI, vol. 14(19), pages 1-17, September.
    16. Hao Chen & Ling He & Jiachuan Chen & Bo Yuan & Teng Huang & Qi Cui, 2019. "Impacts of Clean Energy Substitution for Polluting Fossil-Fuels in Terminal Energy Consumption on the Economy and Environment in China," Sustainability, MDPI, vol. 11(22), pages 1-29, November.
    17. Zeinab Zanjani & Pedro Macedo & Isabel Soares, 2021. "Investigating Carbon Emissions from Electricity Generation and GDP Nexus Using Maximum Entropy Bootstrap: Evidence from Oil-Producing Countries in the Middle East," Energies, MDPI, vol. 14(12), pages 1-22, June.
    18. Ke Yan & Xudong Wang & Yang Du & Ning Jin & Haichao Huang & Hangxia Zhou, 2018. "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy," Energies, MDPI, vol. 11(11), pages 1-15, November.
    19. Abdul Rahim Ridzuan & Aliashim Albani & Abdul Rais Abdul Latiff & Mohamad Idham Md Razak & Mohd Haziq Murshidi, 2020. "The Impact of Energy Consumption based on Fossil Fuel and Hydroelectricity Generation towards Pollution in Malaysia, Indonesia and Thailand," International Journal of Energy Economics and Policy, Econjournals, vol. 10(1), pages 215-227.
    20. Li, Der-Chiang & Chang, Che-Jung & Chen, Chien-Chih & Chen, Wen-Chih, 2012. "Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case," Omega, Elsevier, vol. 40(6), pages 767-773.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:ist:ibsibr:v:50:y:2021:i:1:p:15-46. 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: Istanbul University Press Operational Team (Ertuğrul YAŞAR) (email available below). General contact details of provider: https://edirc.repec.org/data/isisttr.html .

    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.