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    PREDICTING HOUSING SALES IN TURKEY USING ARIMA, LSTM AND HYBRID MODELS
    (Vilnius Gediminas Tech Univ, 2019) Temur, Ayşe Soy; Akgün, Melek; Temur, Gunay
    Having forecast of real estate sales done correctly is very important for balancing supply and demand in the housing market. However, it is very difficult for housing companies or real estate professionals to determine how many houses they will sell next year. Although this does not mean that a prediction plan cannot be created, the studies conducted both in Turkey and different countries about the housing sector are focused more on estimating housing prices. Especially the developing technological advances allow making estimations in many areas. That is why the purpose of this study is both to provide guiding information to the companies in the sector and to contribute to the literature. In this study, a 124-month data set belonging to the 2008 (1)-2018 (4) period has been taken into account for total housing sales in Turkey. In order to estimate the time series of sales, ARIMA (Auto Regressive Integrated Moving Average as linear model), LSTM (Long Short-Term Memory as nonlinear model) has been used. As to increase the estimation, a HYBRID (LSTM and ARIMA) model created has been used in the application. When MAPE (Mean Absolute Percentage Error) and MSE (Mean Squared Error) values obtained from each of these methods were compared, the best performance with the lowest error rate proved to be the HYBRID model, and the fact that all the application models have very close results shows the success of predictability. This is an indication that our study will contribute significantly to the literature.
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    YOLO Object Recognition Algorithm and & x201C;Buy-Sell Decision & x201D; Model Over 2D Candlestick Charts
    (Ieee-Inst Electrical Electronics Engineers Inc, 2020) Birogul, Serdar; Temur, Gunay; Kose, Utku
    Earning via real-time predictions with the experience in the visible trend directions of an investment instrument in the past requires a different perspective on charts. Indicators and formations within the scope of technical analysis constitute the most significant basis of this perspective. Those who can generate a high income in financial markets and even be more successful than large companies are actually the ones interpreting the data in a different way. In this study, a model which had never been encountered in the literature before, was designed through a different perspective on the same data, enabling the movements of an investment element over the 2D candlestick chart to be recognized as a & x201C;Buy-Sell & x201D; object respectively and to decide on the trend direction as a result. The model is trained by state-of-the-art, real-time object detection system (You Only Look Once) YOLO; for the training, one-year candlestick charts belonging to the stocks traded on Borsa & x0130;stanbul (BIST) between 2000 & x2013;2018 were used. The model, which can make a & x201C;Buy-Sell & x201D; decision without the need for an additional time series except for the views on the visual candlestick charts, is promising in terms of its successful predictions. Its ultimate aim is to provide a foresight strengthening the & x201C;Buy-Sell & x201D; decisions to be made in the decision-making process following the other basic and technical analyses in addition to its stand-alone use in making investment decisions. The effect of this foresight on the success can clearly be seen on the test results received. In the results, the model was found to be successful by 85 & x0025; while a 100 & x0025; profit was generated. Besides, the model can be used for all the time series for which candlestick charts can be created.