A Novel Multi-Step Ahead Demand Forecasting Model Based on Deep Learning Techniques and Time Series Augmentation
Subject Areas : ICT
Hossein Abbasimehr
1
*
,
Reza Paki
2
1 - Azarbaijan Shahid Madani University
2 - Azarbaijan Shahid Madani University
Keywords: Time Series, Deep Learning, Long short-term memory, Convolutional network, Multihead self-attention mechanism,
Abstract :
In a business environment where there is fierce competition between companies, accurate demand forecasting is vital. If we collect customer demand data at discrete points in time, we obtain a demand time series. As a result, the demand forecasting problem can be formulated as a time series forecasting task. In the context of time series forecasting, deep learning methods have demonstrated good accuracy in predicting complex time series. However, the excellent performance of these methods is dependent on the amount of data available. For this purpose, in this study, we propose to use time series augmentation techniques to improve the performance of deep learning methods. In this study, three new methods have been used to test the effectiveness of the proposed approach, which are: 1) Long short-term memory, 2) Convolutional network 3) Multihead self-attention mechanism. This study also uses a multi-step forecasting approach that makes it possible to predict several future points in a forecasting operation. The proposed method is applied to the actual demand data of a furniture company. The experimental results show that the proposed approach improves the forecasting accuracy of the methods used in most different prediction scenarios
[1] H. Haberleitner, H. Meyr, and A. Taudes, "Implementation of a demand planning system using advance order information," International Journal of Production Economics, vol. 128, pp. 518-526, 2010.
[2] H. Abbasimehr, M. Shabani, and M. Yousefi, "An optimized model using LSTM network for demand forecasting," Computers & Industrial Engineering, vol. 143, p. 106435, 2020/05/01/ 2020.
[3] M. A. Villegas, D. J. Pedregal, and J. R. Trapero, "A support vector machine for model selection in demand forecasting applications," Computers & Industrial Engineering, vol. 121, pp. 1-7, 2018/07/01/ 2018.
[4] N. J. Johannesen, M. Kolhe, and M. Goodwin, "Relative evaluation of regression tools for urban area electrical energy demand forecasting," Journal of Cleaner Production, vol. 218, pp. 555-564, 2019.
[5] M. Q. Raza, M. Nadarajah, and C. Ekanayake, "Demand forecast of PV integrated bioclimatic buildings using ensemble framework," Applied energy, vol. 208, pp. 1626-1638, 2017.
[6] J. W. Taylor, "A comparison of univariate time series methods for forecasting intraday arrivals at a call center," Management Science, vol. 54, pp. 253-265, 2008.
[7] R. Law, G. Li, D. K. C. Fong, and X. Han, "Tourism demand forecasting: A deep learning approach," Annals of Tourism Research, vol. 75, pp. 410-423, 2019.
[8] F. Martínez, M. P. Frías, M. D. Pérez, and A. J. Rivera, "A methodology for applying k-nearest neighbor to time series forecasting," Artificial Intelligence Review, vol. 52, pp. 2019–2037, 2019.
[9] A. Sagheer and M. Kotb, "Time series forecasting of petroleum production using deep LSTM recurrent networks," Neurocomputing, vol. 323, pp. 203-213, 2019.
[10] I. Maqsood, M. R. Khan, and A. Abraham, "An ensemble of neural networks for weather forecasting," Neural Computing & Applications, vol. 13, pp. 112-122, 2004.
[11] H. Abbasimehr and M. Shabani, "A new framework for predicting customer behavior in terms of RFM by considering the temporal aspect based on time series techniques," Journal of Ambient Intelligence and Humanized Computing, 2020/05/05 2020.
[12] H. Abbasimehr and R. Paki, "Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization," Chaos, Solitons & Fractals, vol. 142, p. 110511, 2021.
[13] K. Bandara, H. Hewamalage, Y.-H. Liu, Y. Kang, and C. Bergmeir, "Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation," arXiv preprint arXiv:2008.02663, 2020.
[14] K. Bandara, C. Bergmeir, and S. Smyl, "Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach," Expert Systems with Applications, vol. 140, p. 112896, 2020/02/01/ 2020.
[15] C. Bergmeir, Rob J. Hyndman, and José M. Benítez, "Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation," International Journal of Forecasting, vol. 32, pp. 303-312, 2016/04/01/ 2016.
[16] A. R. S. Parmezan, V. M. Souza, and G. E. Batista, "Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model," Information Sciences, vol. 484, pp. 302-337, 2019.
[17] H. Shi, S. Hu, and J. Zhang, "LSTM based prediction algorithm and abnormal change detection for temperature in aerospace gyroscope shell," International Journal of Intelligent Computing and Cybernetics, vol. 12, pp. 274-291, 2019.
[18] T. Fischer and C. Krauss, "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, vol. 270, pp. 654-669, 2018.
[19] I. E. Livieris, E. Pintelas, and P. Pintelas, "A CNN–LSTM model for gold price time-series forecasting," Neural computing and applications, vol. 32, pp. 17351-17360, 2020.
[20] S. Mehtab and J. Sen, "Stock price prediction using convolutional neural networks on a multivariate timeseries," arXiv preprint arXiv:2001.09769, 2020.
[21] F. Shahid, A. Zameer, and M. Muneeb, "Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM," Chaos, Solitons & Fractals, vol. 140, p. 110212, 2020/11/01/ 2020.
[22] S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural computation, vol. 9, pp. 1735-1780, 1997.
[23] K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber, "LSTM: A Search Space Odyssey," IEEE Transactions on Neural Networks and Learning Systems, vol. 28, pp. 2222-2232, 2017.
[24] I. Goodfellow, Y. Bengio, and A. Courville, Deep learning: MIT press, 2016.
[25] W. Rawat and Z. Wang, "Deep convolutional neural networks for image classification: A comprehensive review," Neural computation, vol. 29, pp. 2352-2449, 2017.
[26] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al., "Attention is all you need," in 31st International Conference on Neural Information Processing Systems, Long Beach, California, USA, 2017, pp. 6000–6010.
[27] G. E. P. Box and D. R. Cox, "An Analysis of Transformations," Journal of the Royal Statistical Society: Series B (Methodological), vol. 26, pp. 211-243, 1964.
[28] R. B. Cleveland, W. S. Cleveland, J. E. McRae, and I. Terpenning, "STL: A seasonal-trend decomposition," Journal of official statistics, vol. 6, pp. 3-73, 1990.
[29] S. N. Lahiri, Resampling methods for dependent data: Springer Science & Business Media, 2013.