Yazar "Ozcalici, Mehmet" seçeneğine göre listele

Listeleniyor 1 - 5 / 5
  • [ X ]
    Öğe
    Comparison of harmony search derivatives for artificial neural network parameter optimisation: stock price forecasting
    (Inderscience Enterprises Ltd, 2022) Ozcalici, Mehmet; Dosdogru, Ayse Tugba; Ipek, Asli Boru; Gocken, Mustafa
    This study has been conducted on forecasting, as accurately as possible, the next day's stock price using harmony search (HS) and its variants [improved harmony search (IHS), global-best harmony search (GHS), self-adaptive harmony search (SAHS), and intelligent tuned harmony Search (ITHS) together with artificial neural network (ANN)]. The advantage of the proposed models are that the useful information in the original stock data is found by input variable selection and simultaneously the most proper number of hidden neurons in hidden layer is discovered to mitigate overfitting/underfitting problem in ANN. The results have shown that forecasts made by HS-ANN, IHS-ANN, GHS-ANN, SAHS-ANN, and ITHS-ANN demonstrate a tendency to achieve hit rates above 89%, which is considerably better than previously proposed forecasting models in literature. Hence, ANN models provide more valuable forecasting results for investors to hedge against potential risk in stock markets.
  • [ X ]
    Öğe
    Hybridizing Extreme Learning Machine and Bio-Inspired Computing Approaches for Improved Stock Market Forecasting
    (IEEE, 2017) Gocken, Mustafa; Boru, Asli; Dosdogru, Ayse Tugba; Ozcalici, Mehmet
    Under today's economic conditions, developing more robust and realistic forecasting methods is needed to make investments more profitable and secure. However, understanding the structure of the stock markets is very difficult because of the dynamic and non-stationary data. In this context, bio-inspired computing approaches including evolutionary computation and swarm intelligence can be used to make more accurate calculations and forecasting results. This paper improved Extreme Learning Machine (ELM) using Genetic Algorithm (GA), Differential Evolution (DE) as a two evolutionary computation methods, and Particle Swarm Optimization (PSO) and Weighted Superposition Attraction (WSA) as a two swarm intelligence methods for stock market forecasting in Turkey. The results of this study show that proposed methods can be successfully used in any real-time stock market forecasting because of the noteworthy improvement in forecasting accuracy.
  • [ X ]
    Öğe
    Integrating metaheuristics and artificial neural network for weather forecasting
    (Inderscience Enterprises Ltd, 2018) Gocken, Mustafa; Boru, Ash; Dosdogru, Ayse Tugba; Ozcalici, Mehmet
    Over the years, researchers have been analysing to forecast the weather as precisely as possible in order to provide better living conditions. Nevertheless, there is no consensus on the effective weather forecasting methods and therefore, research on providing applicable and effective forecasting methods has been continued. In this study, artificial neural networks (ANNs) are integrated with two metaheuristic methods including genetic algorithm (GA) and harmony search (HS) to determine the most relevant input variables and to search the most appropriate number of hidden neurons. The proposed forecasting methods are implemented for six different cities of Turkey that are selected according to Aydeniz's climate classification. The results of the graphical analysis and performance measures show that daily mean temperature forecasting is improved by GA-ANN and HS-ANN methods due to the ability to capture the advantages of metaheuristic and ANN simultaneously.
  • [ X ]
    Öğe
    Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction
    (Pergamon-Elsevier Science Ltd, 2016) Gocken, Mustafa; Ozcalici, Mehmet; Boru, Asli; Dosdogru, Ayse Tugba
    Stock market price is one of the most important indicators of a country's economic growth. That's why determining the exact movements of stock market price is considerably regarded. However, complex and uncertain behaviors of stock market make exact determination impossible and hence strong forecasting models are deeply desirable for investors' financial decision making process. This study aims at evaluating the effectiveness of using technical indicators, such as simple moving average of close price, momentum close price, etc. in Turkish stock market. To capture the relationship between the technical indicators and the stock market for the period under investigation, hybrid Artificial Neural Network (ANN) models, which consist in exploiting capabilities of Harmony Search (HS) and Genetic Algorithm (GA), are used for selecting the most relevant technical indicators. In addition, this study simultaneously searches the most appropriate number of hidden neurons in hidden layer and in this respect; proposed models mitigate well-known problem of overfitting/underfitting of ANN. The comparison for each proposed model is done in four viewpoints: loss functions, return from investment analysis, buy and hold analysis, and graphical analysis. According to the statistical and financial performance of these models, HS based ANN model is found as a dominant model for stock market forecasting. (C) 2015 Elsevier Ltd. All rights reserved.
  • [ X ]
    Öğe
    Stock price prediction using hybrid soft computing models incorporating parameter tuning and input variable selection
    (Springer London Ltd, 2019) Gocken, Mustafa; Ozcalici, Mehmet; Boru, Asli; Dosdogru, Ayse Tugba
    Over the years, high-dimensional, noisy, and time-varying natures of the stock markets are analyzed to carry out accurate prediction. Particularly, speculators and investors are understandably eager to accurately predict stock price since millions of dollars flow through the stock markets. At this point, soft computing models have empowered them to capture the data patterns and characteristics of stock markets. However, one of the open problems in soft computing models is how to systematically determine architecture of models for given applications. In this study, Harmony Search is utilized to optimize the architecture of Neural Network, Jordan Recurrent Neural Network, Extreme Learning Machine, Recurrent Extreme Learning Machine, Generalized Linear Model, Regression Tree, and Gaussian Process Regression for 1-, 2-, 3-, 5-, 7-, and 10-day-ahead stock price prediction. The experimental results show worthy findings of stock market behavior over different prediction terms and stocks. This study also helps researchers understand which prediction model performed the best and how different conditions affect the prediction accuracy of the models. Proposed hybrid models can be successfully used by speculators and investors to make the investment or to hedge against potential risk in stock markets.