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Deep Learning Based Prediction Model for the Next PurchaseUTKU, A. , AKCAYOL, M. A.
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time series analysis, deep learning, prediction, e-commerce
series(26), time(25), neural(19), forecasting(16), networks(12), learning(11), prediction(9), arima(9), network(8), deep(8)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2020-05-31
Volume 20, Issue 2, Year 2020, On page(s): 35 - 44
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.02005
Web of Science Accession Number: 000537943500005
SCOPUS ID: 85087459081
Time series represent the consecutive measurements taken at equally spaced time intervals. Time series prediction uses the information in a time series to predict future values. The future value prediction is important for many business and administrative decision makers especially in e-commerce. To promote business, sales prediction and sensing of future consumer behavior can help business decision makers in marketing campaigns, budget and resource planning. In this study, deep learning based a new prediction model has been developed for the time of next purchase in e-commerce. The proposed model has been extensively tested and compared with RF, ARIMA, CNN and MLP using a retail market dataset. The experimental results show that the developed model has been more successful than RF, ARIMA, CNN and MLP to predict the time of the next purchase.
|References|||||Cited By «-- Click to see who has cited this paper|
| F. Martinez-Alvarez, A. Troncoso, G. Asencio-Cortes, J. C. Riquelme, "A survey on data mining techniques applied to electricity-related time series forecasting," Energies, vol. 8, no. 11, pp. 13162-13193, 2015. |
[CrossRef] [Web of Science Times Cited 88] [SCOPUS Times Cited 6]
 S. Lahmiri, "A variational mode decompoisition approach for analysis and forecasting of economic and financial time series," Expert Systems with Applications, vol. 55, pp. 268-273, Aug. 2016.
[CrossRef] [Web of Science Times Cited 122] [SCOPUS Times Cited 135]
 R. Adhikari, R. K. Agrawal, "An introductory study on time series modeling and forecasting," LAP Lambert Academic Publishing, Germany, 2013, arXiv.
 I. E. Livieris, E. Pintelas, P. Pintelas, "A CNN-LSTM model for gold price time-series forecasting," Neural Computing and Applications, pp. 1-10.
[CrossRef] [Web of Science Times Cited 152] [SCOPUS Times Cited 183]
 S. Bisgaard, C. L. Jennings, M. Kulahci, "Introduction to time series analysis and forecasting by example," John Wiley & Sons, Canada, pp. 73-171, 2015.
[CrossRef] [SCOPUS Times Cited 126]
 C. Chatfield, "The analysis of time series: an introduction," CRC press, pp. 73-103, 2016.
 G. E. Box, G. M. Jenkins, G. C. Reinsel, G. M. Ljung, "Time series analysis: forecasting and control," John Wiley & Sons, pp. 1-88, 2015.
[CrossRef] [SCOPUS Times Cited 461]
 S. Siami-Namini, N. Tavakoli, A. S. Namin, "A Comparison of ARIMA and LSTM in Forecasting Time Series," In 2018 17th IEEE International Conference on Machine Learning and Applications, Florida, pp. 1394-1401, Dec. 2018.
[CrossRef] [Web of Science Times Cited 194] [SCOPUS Times Cited 306]
 O. Claveria, S. Torra, "Forecasting tourism demand to Catalonia: Neural networks vs. time series models," Economic Modelling, vol. 36, pp. 220-228, Jan. 2014.
[CrossRef] [Web of Science Times Cited 129] [SCOPUS Times Cited 153]
 P. Chujai, N. Kerdprasop, K. Kerdprasop, "Time series analysis of household electric consumption with ARIMA and ARMA models," In: Proceedings of the International MultiConference of Engineers and Computer Scientists, Hong Kong, pp. 295-300, Mar. 2013.
 M. J. Kane, N. Price, M. Scotch, P. Rabinowitz, "Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks," BMC bioinformatics, vol. 15, no. 1, Aug. 2014.
[CrossRef] [Web of Science Times Cited 152] [SCOPUS Times Cited 173]
 R. Fu, Z. Zhang, L. Li, 2016, "Using LSTM and GRU neural network methods for traffic flow prediction," In: Chinese Association of Automation, Youth Academic Annual Conference, Jinzhou, pp. 324-328, Nov. 2016.
[CrossRef] [SCOPUS Times Cited 655]
 M. H. Amini, A. Kargarian, O. Karabasoglu, "ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation," Electric Power Systems Research, vol. 140, pp. 378-390, Nov. 2016.
[CrossRef] [Web of Science Times Cited 177] [SCOPUS Times Cited 204]
 Y. Yang, W. Gao, C. Guo, "Aero-Engine Lubricating Oil Metal Content Prediction Using Non-stationary Time Series ARIMA Model," In: Computational Intelligence and Design, 2017 10th International Symposium, Hangzhou, pp. 51-54, Dec. 2017.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 3]
 J. Zheng, C. Xu, Z. Zhang, X. Li, "Electric load forecasting in smart grids using long-short-term-memory based recurrent neural network," In: 2017 51st Annual Conference on Information Sciences and Systems, Baltimore, pp. 1-6, Mar. 2017.
[CrossRef] [SCOPUS Times Cited 207]
 P. Sobreiro, D. Martinho, A. Pratas, "Sales forecast in an IT company using time series," In: 2018 13th Iberian Conference on Information Systems and Technologies, pp. 1-5, June 2018.
[CrossRef] [SCOPUS Times Cited 4]
 S. McNally, J. Roche, S. Caton, "Predicting the price of Bitcoin using Machine Learning," In: 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing, pp. 339-343, Mar. 2018.
[CrossRef] [Web of Science Times Cited 149] [SCOPUS Times Cited 233]
 H. Wang, K. Wang, H. Zhao, Y. Yue, "Prediction of User Behavior in Smart Home Based on Improved ARIMA Model," In: 2018 IEEE International Conference on Mechatronics and Automation, pp. 298-302, Aug. 2018.
[CrossRef] [SCOPUS Times Cited 6]
 R. Fu, Z. Zhang, L. Li, "Using LSTM and GRU neural network methods for traffic flow prediction," In: 2016 31st Youth Academic Annual Conference of Chinese Association of Automation, pp. 324-328, Nov. 2016.
[CrossRef] [SCOPUS Times Cited 655]
 K. Chen, Y. Zhou, F. Dai, "A LSTM-based method for stock returns prediction: A case study of China stock market," In: 2015 IEEE international conference on big data, pp. 2823-2824, Oct. 2015.
[CrossRef] [SCOPUS Times Cited 294]
 K. K. Sarma, "Neural network based feature extraction for assamese character and numeral recognition," Int. J. Artif. Intell, vol. 2, no. S09, pp. 37-56, 2009.
 C. Pozna, R. E. Precup, J. K. Tar, I. Å krjanc, S. Preitl, "New results in modelling derived from Bayesian filtering," Knowledge-Based Systems, vol. 23, no. 2, pp. 182-194, March 2010.
[CrossRef] [Web of Science Times Cited 54] [SCOPUS Times Cited 60]
 R. A. Gil, Z. C. Johanyak, T. Kovacs, "Surrogate model based optimization of traffic lights cycles and green period ratios using microscopic simulation and fuzzy rule interpolation," Int. J. Artif. Intell, vol. 16, no. 1, pp. 20-40, 2018.
 A. Albu, R. E. Precup, T. A. Teban, "Results and Challenges of Artificial Neural Networks Used for Decision-Making and Control In Medical Applications," Facta Universitatis, Series: Mechanical Engineering, vol. 17, no. 3, pp. 285-308, 2019.
[CrossRef] [Web of Science Times Cited 79] [SCOPUS Times Cited 85]
 R. H. Shumway, D. S. Stoffer, "Time series analysis and its applications with R examples," Springer, pp. 375-376, 2000.
 Y. Zhang, P. Wang, P. Cheng, S. Lei, "Wind speed prediction with wavelet time series based on Lorenz disturbance," Advances in Electrical and Computer Engineering, vol. 17, no.3, pp. 107-115, Aug. 2017.
[CrossRef] [Full Text] [Web of Science Times Cited 26] [SCOPUS Times Cited 27]
 A. Harvey, S. J. Koopman, "Structural time series models," Wiley StatsRef: Statistics Reference Online, New Jersey, ABD, pp. 106-166, 2014.
 M. Valipour, M. E. Banihabib, S. M. R. Behbahani, "Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir," Journal of hydrology, vol. 476, pp. 433-441, Jan. 2013.
[CrossRef] [Web of Science Times Cited 550] [SCOPUS Times Cited 607]
 A. S. Weigend, "Time series prediction: forecasting the future and understanding the past," Routledge, New York, ABD, pp. 345-491 2018.
 W. C. Wang, K. W. Chau, D. M. Xu, X. Y. Chen, "Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition," Water Resources Management, vol. 29, no. 8, pp. 2655-2675, June 2015.
[CrossRef] [Web of Science Times Cited 350] [SCOPUS Times Cited 383]
 G. E. Box, G. M. Jenkins, "Time series analysis, control, and forecasting," San Francisco, CA: Holden Day, New Jersey, ABD, pp. 46-89, 1976.
 M. Langkvist, L. Karlsson, A. Loutfi, "A review of unsupervised feature learning and deep learning for time-series modeling," Pattern Recognition Letters, vol. 42, pp. 11-24, June 2014.
[CrossRef] [Web of Science Times Cited 1064] [SCOPUS Times Cited 767]
 H. I. Fawaz, G. Forestier, J. Weber, L. Idoumghar, P. Muller, "Deep learning for time series classification: a review," Data Mining and Knowledge Discovery, vol. 33, no. 4, pp. 917-963, 2019.
[CrossRef] [Web of Science Times Cited 822] [SCOPUS Times Cited 1065]
 D. Graupe, "Principles of artificial neural networks," World Scientific, World Scientific, Singapore, pp. 9-16, 2013.
 A. Namozov, Y. Cho, "An Efficient Deep Learning Algorithm for Fire and Smoke Detection with Limited Data," Advances in Electrical and Computer Engineering, vol. 18, no. 4, pp.121-129, Nov. 2018.
[CrossRef] [Full Text] [Web of Science Times Cited 31] [SCOPUS Times Cited 50]
 O. Irsoy, E. Alpaydin, "Continuously constructive deep neural networks," IEEE transactions on neural networks and learning systems, 2019.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 8]
 A. Ardakani, C. Condo, W. J. Gross, "Sparsely-connected neural networks: towards efficient VLSI implementation of deep neural networks," arXiv preprint arXiv:1611.01427, 2016.
 J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, H. Lipson, "Understanding neural networks through deep visualization," arXiv preprint arXiv:1506.06579, 2015.
 M. Hussain, J. J. Bird, D. R. Faria, "A study on cnn transfer learning for image classification,". In: UK Workshop on Computational Intelligence, Cham, pp. 191-202, Sept. 2018.
[CrossRef] [Web of Science Times Cited 130] [SCOPUS Times Cited 176]
 K. Park, D. H. Kim, "Accelerating image classification using feature map similarity in convolutional neural networks," Applied Sciences, vol. 9, no.1, 2019.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 19]
 P. Araujo, G. Astray, J. A. Ferrerio-Lage, J. C. Mejuto, J. A. Rodriguez-Suarez, B. Soto, "Multilayer perceptron neural network for flow prediction," Journal of Environmental Monitoring, vol. 13, no. 1, pp. 35-41, 2011.
[CrossRef] [Web of Science Times Cited 24] [SCOPUS Times Cited 28]
 A. Graves, A. R. Mohamed, G. Hinton, "Speech recognition with deep recurrent neural networks," In: 2013 IEEE international conference on Acoustics, speech and signal processing, Vancouver, pp. 6645-6649, May 2013.
[CrossRef] [SCOPUS Times Cited 5610]
 P. Malhotra, L. Vig, G. Shroff, P. Agarwal, "Long short term memory networks for anomaly detection in time series," In: 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, pp.89-94, Apr. 2015.
 X. Qiu, L. Zhang, Y. Ren, P. N. Suganthan, G. Amaratunga, "Ensemble deep learning for regression and time series forecasting," In: 2014 IEEE symposium on computational intelligence in ensemble learning (CIEL), pp. 1-6, Orlando, Dec. 2014.
[CrossRef] [SCOPUS Times Cited 256]
 O. Buyuk, M. L. Arslan, "Combination of Long-Term and Short-Term Features for Age Identification from Voice," Advances in Electrical and Computer Engineering, vol. 18, no. 2, pp. 101-109, May 2018.
[CrossRef] [Full Text] [Web of Science Times Cited 2] [SCOPUS Times Cited 4]
 L. Wang, Y. Zeng, T. Chen, "Back propagation neural network with adaptive differential evolution algorithm for time series forecasting," Expert Systems with Applications, vol. 42, no. 2, pp. 855-863, Feb. 2015.
[CrossRef] [Web of Science Times Cited 350] [SCOPUS Times Cited 408]
 B. Wang, X. Zhu, Q. He, G. Gu, "The forecast on the customers of the member point platform built on the blockchain technology by ARIMA and LSTM," In: 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis, Chengdu, pp. 589-593, Apr. 2018.
[CrossRef] [SCOPUS Times Cited 6]
 https://www.kaggle.com/retailrocket/ecommerce-dataset, (Accessed: Aug. 2019).
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