Demand forecasting application with regression and artificial intelligence methods in a construction machinery company

Author:

Aktepe Adnan,Yanık EmreORCID,Ersöz Süleyman

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Reference64 articles.

1. Abbasimehr, H., Shabani, M., & Yousefi, M. (2020). An optimized model using LSTM network for demand forecasting. Computers & Industrial Engineering, 143, 1–13.

2. Adamowski, J., Fung Chan, H., Prasher, S. O., Ozga-Zielinski, B., & Sliusarieva, A. (2012). Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal (p. 48). Water Resources Research: Canada.

3. Aengchuan, P., & Phruksaphanrat, B. (2018). Comparison of fuzzy inference system (FIS), FIS with artificial neural networks (FIS+ANN) and FIS with adaptive neuro-fuzzy inference system (FIS+ANFIS) for inventory control. Journal of Intelligent Manufacturing, 29, 905–923.

4. Akay, M., Ozsert, G., & George, J. (2014). Prediction of maximal oxygen uptake using support vector machines from submaximal data. Dokuz Eylül University Faculty of Engineering, Journal of Science and Engineering, 16(48), 42–48.

5. Aktepe, A., Yanik, E., & Ersöz, S. (2019). An application of demand forecasting with artificial neural networks in construction machinery sector. In: Proceedings of 10th International Symposium on Intelligent Manufacturing and Service Systems, pp. 78–87.

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