Using Bigdata for Choosing the Right Forecasting Method, Dataset and Period in a Time Series Analysis

Yıl 2024, Cilt: 39 Sayı: 2, 437 - 452, 11.07.2024

Serap Akcan Murat Akcıl Metin Özşahin

https://doi.org/10.21605/cukurovaumfd.1514451

Öz

Nowadays especially production companies gathering a huge data due to their daily transactions on the own systems. Production companies should handle this raw data as handling the raw materials too. Today, scientific studies carried out for this purpose are gathered under the title of BigData. The BigData creates many helps to companies’ competitive advantages according to their competitors. For this view, the purpose of this study was to determine the best demand forecasts method and forecasting period by using BigData at forest production industry. Using the time series analysis module of the WEKA program, the algorithm and data set providing the most accurate estimate for each of the selected decor papers were determined. As a result, it is thought that this study will provide a route map for about choosing right data period and forecasting method for the forest products.

Anahtar Kelimeler

Big data, Data mining, Time series analysis, Demand forecasting, Forest products sector

Zaman Serisi Analizinde Doğru Tahmin Yöntemini, Veri Kümesini ve Dönemi Seçmek İçin Büyük Veriyi Kullanma

Öz

Günümüzde özellikle üretim firmaları kendi sistemleri üzerinde yaptıkları günlük işlemlerden dolayı büyük miktarda veri toplamaktadır. Üretim şirketleri, ham maddeyi ele aldığı gibi bu ham veriyi de ele almalıdır. Günümüzde bu amaçla yapılan bilimsel çalışmalar büyük veri başlığı altında toplanmaktadır. Büyük veri, şirketlerin rakiplerine göre rekabet avantajı sağlamasına birçok katkı sağlamaktadır. Bu doğrultuda bu çalışmanın amacı, orman ürünleri sektöründe büyük veri kullanarak en iyi talep tahmin yöntemini ve tahmin dönemini belirlemektir. Çalışmada, WEKA programının zaman serisi analiz modülü kullanılarak seçilen dekor kağıtlarının her biri için en doğru tahmini sağlayan algoritma ve veri seti belirlenmiştir. Sonuç olarak bu çalışmanın orman ürünlerine ilişkin doğru veri periyodu seçimi ve tahmin yöntemi konusunda bir yol haritası oluşturacağı düşünülmektedir.

Anahtar Kelimeler

Büyük veri, Veri madenciliği, Zaman serisi analizi, Talep tahmini, Orman ürünleri sektörü

Kaynakça

• 1. Ferguson, W.C., Hartley, M.F., Turner, G.B., Pierce, E.M., 1996. Purchasing’s Role in Corporate Strategic Planning. International Journal of Physical Distribution & Logistics Management, 26(4), 51-62.
• 2. Kaes, I., Azeem, A., 2009. Demand Forecasting and Supplier Selection for Incoming Material in RMG Industry: A Case Study. International Journal of Business and Management, 4(5), 149-157.
• 3. Kim, M., Jeong, J., Bae, S., 2019. Demand Forecasting Based on Machine Learning for Mass Customization in Smart Manufacturing. In Proceedings of the 2019 International Conference on Data Mining and Machine Learning, 6-11.
• 4. Arif, M.A.I., Sany, S.I., Nahin, F.I., Rabby, A.S.A., 2019. Comparison Study: Product Demand Forecasting with Machine Learning for Shop. In 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART), 171-176.
• 5. Gupta, S., Sihag, P., 2022. Prediction of the Compressive Strength of Concrete Using Various Predictive Modeling Techniques. Neural Computing and Applications, 34(8), 6535-6545.
• 6. Panarese, A., Settanni, G., Vitti, V., Galiano, A., 2022. Developing and Preliminary Testing of a Machine Learning-Based Platform for Sales Forecasting Using a Gradient Boosting Approach. Applied Sciences, 12, 11054.
• 7. Nasseri, M., Falatouri, T., Brandtner, P., Darbanian, F., 2023. Applying Machine Learning in Retail Demand Prediction—A Comparison of Tree-Based Ensembles and Long Short-Term Memory-Based Deep Learning. Applied Sciences, 13, 11112.
• 8. Aksoy, A., Ozturk, N., Sucky, E., 2012. A Decision Support System for Demand Forecasting in the Clothing Industry. International Journal of Clothing Science and Technology, 24(4), 221-236.
• 9. Yunishafira, A., 2018. Determining the Appropriate Demand Forecasting Using Time Series Method: Study Case at Garment Industry in Indonesia. KnE Social Sciences, 553-564.
• 10. Ren, S., Chan, H.L., Siqin, T., 2020. Demand Forecasting in Retail Operations for Fashionable Products: Methods, Practices, and Real Case Study. Annals of Operations Research, 291(1), 761-777.
• 11. Yadav, A., Ghosh, S., 2019. Forecasting Monthly Farm Tractor Demand for India Using MSARIMA and ARMAX Models. Indian Journal of Agricultural Research, 53(3), 315-320.
• 12. Huber, J., Stuckenschmidt, H., 2020. Daily Retail Demand Forecasting Using Machine Learning with Emphasis on Calendric Special Days. International Journal of Forecasting, 36(4), 1420-1438.
• 13. Spiliotis, E., Makridakis, S., Semenoglou, A.A., Assimakopoulos, V., 2020. Comparison of Statistical and Machine Learning Methods for Daily SKU Demand Forecasting. Operational Research, 22, 3037-3061.
• 14. Panigrahi, S., Behera, H.S., 2020. Time Series Forecasting Using Differential Evolution-Based ANN Modelling Scheme. Arab J Sci Eng, 45, 11129–11146.
• 15. Moroff, N.U., Kurt, E., Kamphues, J., 2021. Machine Learning and Statistics: A Study for Assessing Innovative Demand Forecasting Models. Procedia Computer Science, 180, 40-49.
• 16. Ngo, N.T., Pham, A.D., Truong, T.T.H., 2022. An Ensemble Machine Learning Model for Enhancing the Prediction Accuracy of Energy Consumption in Buildings. Arab J Sci Eng, 47, 4105–4117.
• 17. Pham, Q.B., Kumar, M., DiNunno, F., Elbeltagi, A., Granata, F., Islam, A.R.M., Anh, D.T., 2022. Groundwater Level Prediction Using Machine Learning Algorithms in a Drought-Prone Area. Neural Computing and Applications, 34, 10751-10773.
• 18. Shi, Z., Wang, G., 2018. Integration of Big-Data ERP and Business Analytics (BA). The Journal of High Technology Management Research, 29(2), 141-150.
• 19. Han, J., Kamber, M., 2006. Data Mining: Concepts and Techniques, 2nd. Ed. University of Illinois at Urbana Champaign: Morgan Kaufmann, 735.
• 20. Dwivedi, S., Kasliwal, P., Soni, S., 2016. Comprehensive Study of Data Analytics Tools (Rapidminer, WEKA, R Tool, Knime). In 2016 Symposium on Colossal Data Analysis and Networking (CDAN), 1-8.
• 21. Shevade, S.K., Keerthi, S.S., Bhattacharyya, C., Murthy, K.R.K., 2020. Improvements to the SMO Algorithm for SVM Regression. IEEE Transactions on Neural Networks, 11(5), 1188-1193.
• 22. Witten, I.H., Frank, E., 2002. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Acm Sigmod Record, 31(1), 76-77.
• 23. El-Bendary, N., Elhariri, E., Hazman, M., Saleh, S.M., Hassanien, A.E., 2016. Cultivation-Time Recommender System Based on Climatic Conditions for Newly Reclaimed Lands in Egypt. Procedia Computer Science, 96, 110-119.
• 24. Asaju, L.A.B., Shola, P.B., Franklin, N., Abiola, H.M., 2017. Intrusion Detection System on a Computer Network Using an Ensemble of Randomizable Filtered Classifier, K-Nearest Neighbor Algorithm. FUW Trends in Science & Technology Journal, 2(1), 550-553.
• 25. Pal, S.K., Mitra, S., 1992. Multilayer Perceptron, Fuzzy Sets, Classification. IEEE Transactions on Neural Networks, 3(5), 683-697.
• 26. Mirmozaffari, M., Alinezhad, A., Gilanpour, A., 2017. Data Mining Classification Algorithms for Heart Disease Prediction. Int'l Journal of Computing, Communications & Inst. Engg, 4(1), 11-15.
• 27. Lin, W., Wu, Z., Lin, L., Wen, A., Li, J., 2017. An Ensemble Random Forest Algorithm for Insurance Big Data Analysis. IEEE Access, 5, 16568-16575.
• 28. Pratola, M.T., Chipman, H.A., Gattiker, J.R., Higdon, D.M., McCulloch, R., Rust, W.N., 2014. Parallel Bayesian Additive Regression Trees. Journal of Computational and Graphical Statistics, 23(3), 830-852.
• 29. Yildirim, I., Ozsahin, S., Okan, O.T., 2014. Prediction of Non-Wood Forest Products Trade Using Artificial Neural Networks. J. Agr. Sci. Tech., 16, 1493-1504.
• 30. Lin, M., Zang, Z., Cao, Y., 2019. Forecasting Supply and Demand of the Wooden Furniture Industry in China. Forest Products Journal, 69 (3), 228-238.