Using Bigdata for Choosing the Right Forecasting Method, Dataset and Period in a Time Series Analysis-Reference-Cited by-同舟云学术

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

Published:2024-07-11 Issue:2 Volume:39 Page:437-452
ISSN:2757-9255
Container-title:Çukurova Üniversitesi Mühendislik Fakültesi Dergisi
language:
Short-container-title:

Author:

Akcan Serap¹^ORCID,Akcıl Murat²^ORCID,Özşahin Metin³^ORCID

Affiliation:

1. TARSUS ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ENDÜSTRİ MÜHENDİSLİĞİ BÖLÜMÜ

2. SÜLEYMAN DEMİREL ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ENDÜSTRİ MÜHENDİSLİĞİ BÖLÜMÜ

3. OSMANİYE KORKUT ATA ÜNİVERSİTESİ, İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ, YÖNETİM BİLİŞİM SİSTEMLERİ BÖLÜMÜ, YÖNETİM BİLİŞİM SİSTEMLERİ PR.

Abstract

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.

Publisher

Cukurova Universitesi Muhendislik-Mimarlik Fakultesi Dergisi

Reference30 articles.

1. 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. 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. 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. 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. 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.