Kumar, A., Shankar, R., & Aljohani, N. R. (2020). A big data driven framework for demand-driven forecasting with effects of marketing-mix variables. Industrial marketing management, 90, 493-507.
 Villegas, M. A., Pedregal, D. J., & Trapero, J. R. (2018). A support vector machine for model selection in demand forecasting applications. Computers & industrial engineering, 121, 1-7. https://doi.org/10.1016/j.cie.2018.04.042.
 Johannesen, N. J., Kolhe, M., & Goodwin, M. (2019). Relative evaluation of regression tools for urban area electrical energy demand forecasting. Journal of cleaner production, 218, 555-564.
 Law, R., Li, G., Fong, D. K. C., & Han, X. (2019). Tourism demand forecasting: A deep learning approach. Annals of tourism research, 75, 410-423
 Olaniyi, A. A., Adedotun, K. O., & Samuel, O. A. (2018). Forecasting methods for domestic air passenger demand in Nigeria. Journal of applied research on industrial engineering, 5(2), 146-155. 10.22105/jarie.2018.133561.1038
 Bandara, K., Bergmeir, C., & Smyl, S. (2020). Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach. Expert systems with applications, 140, 112896. https://doi.org/10.1016/j.eswa.2019.112896
 Sagheer, A., & Kotb, M. (2019). Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing, 323, 203-213.
 Panigrahi, S., & Behera, H. S. (2017). A hybrid ETS–ANN model for time series forecasting. Engineering applications of artificial intelligence, 66, 49-59.
 Parmezan, A. R. S., Souza, V. M., & Batista, G. E. (2019). 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, 484, 302-337.
 Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
 Wu, Y., Yuan, M., Dong, S., Lin, L., & Liu, Y. (2018). Remaining useful life estimation of engineered systems using vanilla LSTM neural networks. Neurocomputing, 275, 167-179. https://doi.org/10.1016/j.neucom.2017.05.063.
 Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied soft computing, 11(2), 2664-2675.
 Murray, P. W., Agard, B., & Barajas, M. A. (2018). Forecast of individual customer’s demand from a large and noisy dataset. Computers & industrial engineering, 118, 33-43.
 Abbasimehr, H., & Shabani, M. (2020). 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. 10.1007/s12652-020-02015-w.
 Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European journal of operational research, 270(2), 654-669.
 Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, 5(2), 157-166.
 Xin, W. A. N. G., Ji, W. U., Chao, L. I. U., Haiyan, Y. A. N. G., Yanli, D. U., & Wensheng, N. I. U. (2018). Exploring LSTM based recurrent neural network for failure time series prediction. Journal of beijing university of aeronautics and astronautics, 44(4), 772-784.
 Abbasimehr, H., Shabani, M., & Yousefi, M. (2020). An optimized model using LSTM network for demand forecasting. Computers & industrial engineering. https://doi.org/10.1016/j.cie.2020.106435.
 Shankar, S., Ilavarasan, P. V., Punia, S., & Singh, S. P. (2019). Forecasting container throughput with long short-term memory networks. Industrial management & data systems. 120(3), 425-441. 10.1108/IMDS-07-2019-0370.
 Ke, J., Zheng, H., Yang, H., & Chen, X. M. (2017). Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach. Transportation research part C: Emerging technologies, 85, 591-608.
 Pan, B., Yuan, D., Sun, W., Liang, C., & Li, D. (2018, June). A novel LSTM-Based Daily Airline Demand Forecasting Method Using Vertical and Horizontal Time series. Pacific-Asia conference on knowledge discovery and data mining (pp. 168-173). Springer, Cham.
 Bedi, J., & Toshniwal, D. (2018). Empirical mode decomposition based deep learning for electricity demand forecasting. IEEE access, 6, 49144-49156.
 Bouktif, S., Fiaz, A., Ouni, A., & Serhani, M. A. (2018). Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches. Energies, 11(7), 1636.
 Bedi, J., & Toshniwal, D. (2019). Deep learning framework to forecast electricity demand. Applied energy, 238, 1312-1326.
 Su, H., Zio, E., Zhang, J., Xu, M., Li, X., & Zhang, Z. (2019). A hybrid hourly natural gas demand forecasting method based on the integration of wavelet transform and enhanced Deep-RNN model. Energy. 178, 585-597. https://doi.org/10.1016/j.energy.2019.04.167
 Tan, M., Yuan, S., Li, S., Su, Y., Li, H., & He, F. (2019). Ultra-short-term industrial power demand forecasting using LSTM based hybrid ensemble learning. IEEE transactions on power systems, 35(4), 2937-2948. 10.1109/TPWRS.2019.2963109
 Kulshrestha, A., Krishnaswamy, V., & Sharma, M. (2020). Bayesian BILSTM approach for tourism demand forecasting. Annals of tourism research, 83, 102925. https://doi.org/10.1016/j.annals.2020.102925
 Punia, S., Nikolopoulos, K., Singh, S. P., Madaan, J. K., & Litsiou, K. (2020). Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail. International journal of production research, 1-16. 10.1080/00207543.2020.1735666
 Bui, V., Kim, J., & Jang, Y. M. (2020, February). Power demand forecasting using long short-term memory neural network based smart grid. 2020 international conference on artificial intelligence in information and communication (ICAIIC) (pp. 388-391). IEEE.
 Wu, D. C. W., Ji, L., He, K., & Tso, K. F. G. (2020). Forecasting tourist daily arrivals with a hybrid Sarima–Lstm approach. Journal of hospitality & tourism research. https://doi.org/10.1177/1096348020934046
 Chollet, F. (2015). Keras. Retrived January 12, 2020 from https://github.com/fchollet/keras.
 Graves, A. (2013). Generating sequences with recurrent neural networks. https://arxiv.org/
 Jiawei Han, M. K., & Pei, J. (2011). Data mining: concepts and techniques: concepts and techniques. Waltham, USA: Elsevier Science.
 Martínez, F., Frías, M. P., Pérez-Godoy, M. D., & Rivera, A. J. (2018). Dealing with seasonality by narrowing the training set in time series forecasting with kNN. Expert systems with applications, 103, 38-48.