Multi-layer neural networks for sales forecasting

• Autorzy: Scherer M.

Identyfikatory

DOI

10.17512/jamcm.2018.1.06

• Języki publikacji: EN

Abstrakty

EN

Predicting business operations on the basis of previous events plays an important role in managing a company. In the paper, we predict monthly sales volume of a textile warehouse by mathematical tools. To this end we use a feedforward artificial neural network trained on past data. The network predicted the volume with high accuracy. For the examined company, such prediction is very important as nearly the entire range of products is imported from different countries and the goods have to be ordered in advance.

Słowa kluczowe

EN

data forecasting; machine learning; artificial neural networks

PL

sztuczna sieć neuronowa; uczenie maszynowe; systemy neuro-rozmyte

• Wydawca: Wydawnictwo Politechniki Częstochowskiej
• Czasopismo: Journal of Applied Mathematics and Computational Mechanics
• Rocznik: 2018
• Tom: Vol. 17, nr 1
• Strony: 61--68
• Opis fizyczny: Bibliogr. 19 poz. rys.

Twórcy

autor

Scherer M.

• Department of Engineering Management, Czestochowa University of Technology Czestochowa, Poland

Bibliografia

• [1] Tao, R., Yuan, D.C., & Hu, G.H. (2014). BP neural network based animation production prediction. Applied Mechanics and Materials, 539, 475-478.
• [2] Scherer, M. (2017). Waste flows management by their prediction in a production company. Journal of Applied Mathematics and Computational Mechanics, 16, 2, 135-144.
• [3] Konovalova, N., Kristovska, I., & Kudinska, M. (2016). Credit risk management in commercial banks. Polish Journal of Management Studies, 13, 2, 90-100.
• [4] Scherer, M., Smoląg, J., & Gawęda, A. (2016). Predicting Success of Bank Direct Marketing by Neuro-fuzzy Systems. 15th International Conference on Artificial Intelligence and Soft Computing. Part II (ICAISC 2016), Cham: Springer International Publishing, 570-576.
• [5] Deliana, Y., & Rum, I.A. (2017). Understanding consumer loyalty using neural network. Polish Journal of Management Studies, 16, 2, 51-61.
• [6] Surujlal, J., & Dhurup, M. (2017). Antecedents predicting coaches’ intentions to remain in sport organisations. Polish Journal of Management Studies, 16, 1, 234-247.
• [7] Szarek, A., Korytkowski, M., Rutkowski, L., Scherer, R., & Szyprowski, J. (2012). Application of neural networks in assessing changes around implant after total hip arthroplasty. In International Conference on Artificial Intelligence and Soft Computing, Berlin: Springer, 335-340.
• [8] Villmann, T., Bohnsack, A., & Kaden, M. (2017). Can learning vector quantization be an alternative to svm and deep learning? - Recent trends and advanced variants of learning vector quantization for classification learning. Journal of Artificial Intelligence and Soft Computing Research, 7, 1, 65-81.
• [9] Scherer, R. (2009). Neuro-fuzzy relational systems for nonlinear approximation and prediction. Nonlinear Analysis, 71, e1420-e1425.
• [10] Nikulin, V. ( ). Prediction of the shoppers loyalty with aggregated data streams. Journal of Artificial Intelligence and Soft Computing Research, 6, 2, 69-79.
• [11] Rivero, C.R., Pucheta, J., Laboret, S., Sauchelli, V., & Patińo, D. (2017). Energy associated tuning method for short-term series forecasting by complete and incomplete datasets. Journal of Artificial Intelligence and Soft Computing Research, 7, 1, 5-16.
• [12] Chang, O., Constante, P., Gordon, A., & Singana, M. (2017). A novel deep neural network that uses space-time features for tracking and recognizing a moving object. Journal of Artificial Intelligence and Soft Computing Research, 7, 2, 125-136.
• [13] Korytkowski, M., Nowicki, R., Rutkowski, L., & Scherer, R. (2011). AdaBoost Ensemble of DCOG Rough-Neuro-Fuzzy Systems, In Computational Collective Intelligence, Technologies and Applications, P. Jedrzejowicz, N. Nguyen, K. Hoang, Eds., Berlin/Heidelberg: Springer, 62-71.
• [14] Bishop, Ch.M. (1995). Neural Networks for Pattern Recognition. Oxford University Press.
• [15] Ke, Y., & Hagiwara, M. (2017). An English neural network that learns texts, finds hidden knowledge, and answers questions. Journal of Artificial Intelligence and Soft Computing Research, 7, 4, 229-242.
• [16] Bologna, G., & Hayashi, Y. (2017). Characterization of symbolic rules embedded in deep DIMLP networks: a challenge to transparency of deep learning. Journal of Artificial Intelligence and Soft Computing Research, 7, 4, 265-286.
• [17] Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986). Learning representations by back-propagating errors, Nature. 323, 6088, October, 533-536.
• [18] Scherer, R. (2012). Multiple Fuzzy Classification Systems. Springer,.
• [19] Scherer, R, & Rutkowski, L. (2002), Neuro-Fuzzy Relational Systems. International Conference on Fuzzy Systems and Knowledge Discovery, Singapore, 44-48.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).