Abstract
The customer consumption pattern prediction has become one of a significant role in developing the business and taking it to a competitive edge. For forecasting the behaviors of the consumers the paper engages an artificial recurrent neural network architecture the long short-term memory an improvement of recurrent neural network. The mechanism laid out to predict the pattern of the consumption, uses the information's about the consumption of products based on the age and the gender. The information essential are extracted and described with the prefix-span procedure based association rule. Utilizing the information about the day to day products purchase pattern as input a frame work to predict the customer daily essentials was designed, the designed frame was capable enough to learn the dissimilarities across the predicted and the original miscalculation rates. The frame work devised was tested using real life applications and the results observed demonstrated that the proposed LSTM based prediction with the prefix span association rule to acquire the day today consumption details is compatible for forecasting the customer consumption over time accurately.
References
Gan, Christopher, V. Limsombunchao, Michael D. Clemes, and Yong YA Weng. "Consumer choice prediction: Artificial neural networks versus logistic models." (2005).
Afolabi, Ibukun T., Olufunke Oladipupo, Rowland E. Worlu, and I. O. Akinyemi. "A systematic review of consumer behaviour prediction studies." Covenant Journal of Business and Social Sciences 7, no. 1 (2016).
Valecha, Harsh, Aparna Varma, Ishita Khare, Aakash Sachdeva, and Mukta Goyal. "Prediction of Consumer Behaviour using Random Forest Algorithm." In 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), pp. 1-6. IEEE, 2018.
Krebs, Florian, Bruno Lubascher, Tobias Moers, Pieter Schaap, and Gerasimos Spanakis. "Social emotion mining techniques for Facebook posts reaction prediction." arXiv preprint arXiv:1712.03249 (2017).
Urso, Alfonso, Antonino Fiannaca, Massimo La Rosa, Valentina Ravì, and Riccardo Rizzo. "Data mining: Classification and prediction." Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics (2018): 384.
Sherstinsky, Alex. "Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network." arXiv preprint arXiv:1808.03314 (2018).
Staudemeyer, Ralf C., and Eric Rothstein Morris. "Understanding LSTM--a tutorial into Long Short-Term Memory Recurrent Neural Networks." arXiv preprint arXiv:1909.09586 (2019).
Qing, Xiangyun, and Yugang Niu. "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM." Energy 148 (2018): 461-468.
Mane, Rashmi V., and V. R. Ghorpade. "Predicting student admission decisions by association rule mining with pattern growth approach." In 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT), pp. 202-207. IEEE, 2016.
Raj, Jennifer S. "A Comprehensive Survey on the Computational Intelligence Techniques and Its Applications." Journal of ISMAC 1, no. 03 (2019): 147-159.
Joseph, S. Iwin Thanakumar, and Iwin Thanakumar. "Survey of data mining algorithm’s for intelligent computing system." Journal of trends in Computer Science and Smart technology (TCSST) 1, no. 01 (2019): 14-24.
Raj, Jennifer S., and J. Vijitha Ananthi. "Recurrent Neural Networks and Nonlinear Prediction in Support Vector Machines." Journal of Soft Computing Paradigm (JSCP) 1, no. 01 (2019): 33-40.
Bashar, Abul. "Survey on Evolving Deep Learning Neural Network Architectures." Journal of Artificial Intelligence 1, no. 02 (2019): 73-82.