___

• Açıcı, K., Aşuroğlu, T., & Oğul, H. (2017). Information retrieval in metal music sub-genres. 2017 25th Signal Processing and Communications Applications Conference (SIU). Antalya, TURKEY: IEEE.
• Adeva, J. J., Beresi, U. C., & Calvo, R. A. (2005). Accuracy and Diversity in Ensembles of Text Categorisers. CLEI Electronic Journal, 8(2), 1-12. doi:DOI: 10.19153/cleiej.12.1.1
• Ahmetoğlu, M. E. (2018). Veri Merkezi Sektöründe Satış Tahminleme Modeli ve Özel Kurum Üzerinde Ugulaması. İstanbul: Bahçeşehir Üniversitesi.
• Aladağ, Ç. H., Eğrioğlu, E., & Kadılar, C. (2009). Forecasting Nonlinear Time Series with a Hybrid Methodology. Applied Mathematics Letters(22), 1467-1470. doi:https://doi.org/10.1016/j.aml.2009.02.006
• Albayrak, A. S. (2010). ARIMA Forecasting of Primary Energy Production and Consumption in Turkey: 1923–2006. Enerji, Piyasa ve Düzenleme, 24-50.
• Armano, G., Marchesi, M., & Murru, A. (2005). A Hybrid Genetic-Neural Architecture for Stock Indexes Forecasting. Information Sciences(170), 3-33.
• Ateşonğun, A. A. (2015). ARIMA ve Yapay Sinir Ağları (YSA) Kullanılarak Hibrit Tahmin Modeli Geliştirilmesi. Ankara: Başkent Üniversitesi Fen Bilimleri Enstitüsü.
• Atienza, R. (Posted on 2017, 4 26). LSTM by Example using Tensorflow (Text Generate). towardsdatascience.com: https://towardsdatascience.com/lstm-by-example-using-tensorflow-feb0c1968537
• Babu, C. N., & Reddy, B. E. (2014). A Moving-Average Filter Based Hybrid ARIMA–ANN Model for Forecasting Time Series Data. Applied Soft Computing(23), 27-38. doi:https://doi.org/10.1016/j.asoc.2014.05.028
• Bengio, Y., Patrice, S., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE transaction on neural networks, 157-166.
• Bengio, Y., Simard, P., & Franscon, P. (2015). Learning Long-Term Dependencies with Gradient Descent is Diffucult. IEEE Trans. Neural Networks, 5(2), 157-166.
• Bhardwaj, S., M.Gadre, V., & Chandrasekhar, E. (2020). Statistical Analysis of DWT Coefficients of fGn Processes Using ARFIMA (p,d,q) Models. Physica A: Statistical Mechanics and its Applications, 547, 124404. doi:10.1016/j.physa.2020.124404
• Bircan, H., & Karagöz, Y. (2003). Box-Jenkins Modelleri ile Aylık Döviz Kuru Tahmini Üzerine Bir Uygulama. Kocaeli Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 6(2), 49-62. Şubat 10, 2017 tarihinde http://dergipark.gov.tr/download/article-file/252067
• Box, P., & Jenkins, G. (1976). Time Series Analysis:Forecasting and Control. Holden-day Inc.
• Brownlee, J. (2020). Machine Learning Mastery. https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/, Jully, 2020.
• Bukhari, A. H., Raja, M. A., Sulaiman, M., Islam, S., Shoaib, M., & Kumam, P. (2020). Fractional Neuro-Sequential ARFIMA-LSTM for Financial Market Forecasting. IEEE ACCESS, 8, 71326-71338. doi:10.1109/ACCESS.2020.2985763
• Büyükşahin, Ü., & Ertekin, Ş. (2018). Ampirik Kip Ayrışımı ve Hibrit Metot Kullanarak Zaman Serisi Tahmini. Sinyal İşleme ve İletişim Uygulamaları (SİU) Kurultayı. IEEE.
• Chollet, F. a. (2015). Keras. chollet2015keras. \url{https://keras.io, 2020.04.12
• Contreras, J., Espinola, R., Nogales, F., & Conejo, A. (2003). ARIMA models to predict next-day electricity prices. IEEE Tans. Power Syst., 1014-1020.
• Düzyol, M. (1997). Türkiye’de Bina İnşaatı Sektörü ve 1990-2010 Dönemi Bölgesel İhtiyaç Tahmini. Ankara: DPT: 2484.
• Ediger, V. Ş., & Akar, S. (2007). ARIMA Forecasting of Primary Energy Demand by Fuel in Turkey. Energy Policy, 1701-1708. doi:https://doi.org/10.1016/j.enpol.2006.05.009
• Erdoğdu, E. (2007). Electricity Demand Analysis Using Cointegration and ARIMA Modelling: A Case Study of Turkey. Energy Policy, 1129-1146. doi:https://doi.org/10.1016/j.enpol.2006.02.013
• Fattah, J., Ezzine, L., Aman, Z., Moussami, H. E., & Lachhab, A. (2018). Forecasting of Demand Using ARIMA Model. International Journal of Engineering Business Management, 10, 1-9. doi:10.1177/1847979018808673
• Greenwood, J., & Hercowitz, Z. (1991). The Allocation of Capital and The Time Over The Business Cycle. Journal of Political Economy(99), 1188-1214. doi:http://dx.doi.org/10.1086/261797
• He, G., & Deng, Q. (2012). A Hybrid ARIMA and Neural Network Model to Forecast Particulate. Matter Concentration in Changsha. https://www.isiaq.org/docs/PDF%20Docs%20for%20Proceedings/1F.3.pdf
• Hocaoğlu, F. O., Kaysal, K., & Kaysal, A. (2015). Yük Tahmini İçin Hibrit (YSA ve Regresyon) Model. Akademik Platform, 33-39. doi:DOI: 10.5505/apjes.2015.94695
• Hochreiter S. (1998). The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based System, 107-116.
• Hochreiter, S., & Schmidhuber, J. (1997). Long Sort Term Memory. Neural Computation, 1735-1780.
• Ioannou, K., Birbilis, D., & Lefakis, P. (2011). A Method for Predicting the Possibility of Ring Shake Appearance on Standing Chestnut Trees. Journal of Environmental Protection and Ecology, 295-304. https://docs.google.com/a/jepe-journal.info/viewer?a=v&pid=sites&srcid=amVwZS1qb3VybmFsLmluZm98amVwZS1qb3VybmFsfGd4OjJkYzQwODIyZjE4ZmJmMzQ
• Kang, E. (2018). Github: https://github.com/llSourcell/LSTM_Networks/blob/master/LSTM%20Demo.ipynb
• Khandelwal, I., Adhikari, R., & Verma, G. (2015). Time Series Forecasting using Hybrid ARIMA and ANN Models based on DWT Decomposition. Procedia Computer Science(48), 173-179.
• Khashei M., H. S. (2008). A New Hybrid Artificial Neural Networks and Fuzzy Regression Model for Time Series Forecasting. Fuzzy Sets and Systems(159), 769-786. doi:https://doi.org/10.1016/j.fss.2007.10.011
• Khashei, M., & Bijari, M. (2011). A Novel Hybridization of Artificial Neural Networks and ARIMA Models for Time Series Forecasting. Applied Soft Computing(11), 2664-2675.
• Khashei, M., Hejazi, S. R., & Bijari, M. (2008). A New Hybrid Artificial Neural Networks and Fuzzy Regression Model for Time Series Forecasting. Fuzzy Sets and Systems(159), 769-786. doi:https://doi.org/10.1016/j.fss.2007.10.011
• Kılıç, R., & Demirbaş, E. (2012). Türkiye’de Kamu İnşaat Harcamalarının Belirleyicileri ile Ekonomik Büyüme Arasındaki İlişki. Akademik Yaklaşımlar Dergisi, 3(2), 84-97.
• Kim, H., & Shin, K. (2007). A Hybrid Approach Based on Neural Networks and Genetic Algorithms for Detecting Temporal Patterns in Stock Markets. Applied Soft Computing(7), 569-576.
• Koutroumanidis, T., Ioannou, K., & Zafeiriou, E. (2011). Forecasting Bank Stock Market Prices with A Hybrid Method: The Case of Alpha Bank. Journal of Business Economics and Management, 12(1), 144-163. doi:https://doi.org/10.3846/16111699.2011.555388
• Koutroumanidis, T., Ioannoub, K., & Arabatzis, G. (2009). Predicting Fuelwood Prices in Greece With the Use of ARIMA Models, Artificial Neural Networks and a Hybrid ARIMA–ANN Model. Energy Policy(37), 3627-3634. doi:https://doi.org/10.1016/j.enpol.2009.04.024
• Lin, T., Guo, T., & Aberer, K. (2017). Hybrid Neural Networks for Learning the Trend in Time Series. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, (s. 2273-2279). Melbourne, Australia. doi:https://doi.org/10.24963/ijcai.2017/316
• Luxhoj, J., Riis, J., & Stensballe, B. (1996). A Hybrid Econometric-Neural Network Modeling Approach for Sales Forecasting. International Journal of Production Economics(45), 175-192.
• Namın, S. S., & Namın, A. S. (2018). Forecasting Economic and Financial Time Series: ARIMA vs. LSTM. Lubbock, TX, USA: Texas Tech University. doi:https://arxiv.org/ftp/arxiv/papers/1803/1803.06386.pdf
• Newbold, P. (1983). ARIMA Model Building and The Time Series Analysis Approach to Forecasting. Journal of Forecasting, 2(1), 23-35. doi: https://doi.org/10.1002/for.3980020104
• Olah, C. (2015, August 27). Understanging LSTM Networks. Nisan 10, 2019 tarihinde colah.github.io: colah.github.io/posts/2015-08-Understanding-LSTMs/.
• Oliveira, M., & Torgo, L. (2014). Ensembles for Time Series Forecasting. In Proceedings of the Sixth Asian Conference on Machine Learning, (s. 360-370). http://proceedings.mlr.press/v39/oliveira14.pdf adresinden alındı
• Opitz, D., & Maclin, R. (1999). Popular Ensemble Methods: An Empirical Study. Journal of Artificial Intelligence Research, 11, 169–198. doi:https://doi.org/10.1613/jair.614
• Pablo, B. J., Hilda, C., Xavier, A., Diego, J. J., Felipe, S., & Henry, B. (2016). Artificial Neural Network and Monte Carlo Forecasting with Generation of L-Scenarios. 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing ond Communications, Cloud and Big Data Computing, Internet of People and Smart World Congress, (s. 665-670). doi:10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0110
• Pagourtzi, E., Assimakolopulos, V., Hatzichristons, T., & French, N. (2003). Real Estate Appraisal: A Review of Valuation Methods. Journal of Property Investment & Finance, 21(4), 383-401.
• Papagera, A., Ioannou, K., Zaimes, G., Iakovoglou, V., & Simeonidou, M. (2014). Simulation and Prediction of Water Allocation Using Artificial Neural Networks and a Spatially Distributed Hydrological Model. Agris on-line Papers in Economics and Informatics, 6(4), 101-111. https://ageconsearch.umn.edu/record/196580/.
• Pascanu, R., Mikolov, T., & Bengio, Y. (2013). On the difficult of training recurrent neural networks. ICML, 1310-1318.
• Prieto, M. P., Uribbarri, P. M., & Tardioli, G. (2019). Applying Modeling and Optimization Tools to Existing City Quarters. Urban Energy Systems for Low-Carbon Cities, 333-414. doi:10.1016/B978-0-12-811553-4.00010-X
• Rossum, G. V. (2001). Python. (T. M. Python) https://docs.python.org/3/2019.05.17
• Sallehuddin, R., Shamsuddin, S. M., Hashim, S. Z., & Abraham, A. (2007). Forecasting Time Series Data Using Hybrid Grey Relational Artificial Neural Network and Auto Regressive Integrated Moving Average Model. ICS AS CR 2007, (s. 573-605). https://www.researchgate.net/publication/287522177_Forecasting_time_series_data_using_hybrid_grey_relational_artificial_neural_network_and_auto_regressive_integrated_moving_average_model.
• Sarı, M. (2016, Ocak). Yapay Sinir Ağları ve Bir Otomotiv Firmasında Satış Talep Tahmini Uygulaması. Yüksek Lisans Tezi. Sakarya, Türkiye: Sakarya University.
• Smith, M. J. (2018). Statistical Analysis (2 b.). Edinburgh: The Winchelsea Press, Drumlin Security Ltd. https://www.statsref.com/StatsRefSample.pdf
• Soy Temür, A., Akgün, M., & Temür, G. (2019). Predicting Housing Sales In Turkey Using Arima, Lstm And Hybrid Models. Journal of Business Economics and Management, 20(5), 920-938. doi:https://doi.org/10.3846/jbem.2019.10190
• Sugiartawan, P., Pulungan, R., & Sari, A. K. (2017). Prediction by a Hybrid of Wavelet Transform and Long-Short-Term-Memory Neural Network. International Journal of Advanced Computer Science and Applications, 8(2), 326-332. https://www.researchgate.net/profile/Reza_Pulungan/publication/314166897_Prediction_by_a_Hybrid_of_Wavelet_Transform_and_Long-Short-Term-Memory_Neural_Network/links/5950a68f45851543383a3d3b/Prediction-by-a-Hybrid-of-Wavelet-Transform-and-Long-Short-Term-M.
• Suresh, K., & Priya, K. (2011). Forecasting sugarcaneyield of Tamilnadu using ARIMA models. Sugar Tech., 23-26.
• Tsaih, R., Hsu, Y., & Lai, C. (1998). Forecasting S&P 500 Stock Index Futures with A Hybrid Al System. Decision Support Systems(23), 161-174.
• Tseng, F., Yu, H., & Tzeng, G. (2002). Combining Neural Network Model with Seasonal Time Series ARIMA Model. Tecnological Forecasting&Social Change(69), 71-87.
• Vasques, X., Vanel, L., Villette, G., & Cif, L. (2016). Morphological Neuron Classification Using Machine Learning. Frontiers in Neuroanatomy, 10, 102-114. doi:10.3389/fnana.2016.00102
• Wedding, D., & Cios, K. (1996). Time Series Forecasting by Combining RBF Networks, Certainy Factors and The Box-Jenkins Model. Neurocomputing, 10, 149-168.
• Wu, L., & Brynjolfsson, E. (2015). The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales. S. M. Avi Goldfarb içinde, Economic Analysis of the Digital Economy (s. 89-118). Chicago, USA: University of Chicago Press. doi:http://dx.doi.org/10.2139/ssrn.2022293
• Yılmazel, Ö., Afşar, A., & Yılmazel, S. (2018). Konut Fiyat Tahmininde Yapay Sinir Ağları Yönteminin Kullanılması. UİİİD-IJEAS, 285-300.
• Yu, L., Jiao, C., Xin, H., Wang, Y., & Wang, K. (2018). Prediction on Housing Price Based on Deep Learning. World Academy of Science, Engineering and Technology, 12(2). doi:doi.org/10.5281/zenodo.1315879
• Yu, L., Wang, S., & Lai, K. (2005). A Novel Nonlinear Ensemble Forecasting Model Incorporating GLAR and ANN for Foreign Exchange Rates. Computers&Operations Research(32), 2523-2541.
• Zhang, G. (2003). Time Series Forecasting Using a Hybrid ARIMA and Neural Netwok Model. Neurocomputing(50), 159-175. doi:https://doi.org/10.1016/S0925-2312(01)00702-0
• Zhou, Z., & Hu, C. (2008). An Effective Hybrid Approach Based on Grey and Arma for Forecasting Gyro Drift. Chaos, Solitons and Fractals(35), 525-529.