• The Journal of Asian Finance, Economics and Business
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• 제7권10호
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• pp.9-21
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• 2020

The aim of this paper is to predict the Borsa Istanbul (BIST) 30 index movements to determine the most accurate buy and sell decisions using the methods of Artificial Neural Networks (ANN) and Genetic Algorithm (GA). We combined these two methods to obtain a hybrid intelligence method, which we apply. In the financial markets, over 100 technical indicators can be used. However, several of them are preferred by analysts. In this study, we employed nine of these technical indicators. They are moving average convergence divergence (MACD), relative strength index (RSI), commodity channel index (CCI), momentum, directional movement index (DMI), stochastic oscillator, on-balance volume (OBV), average directional movement index (ADX), and simple moving averages (3-day moving average, 5-day moving average, 10-day moving average, 14-day moving average, 20-day moving average, 22-day moving average, 50-day moving average, 100-day moving average, 200-day moving average). In this regard, we combined these two techniques and obtained a hybrid intelligence method. By applying this hybrid model to each of these indicators, we forecast the movements of the Borsa Istanbul (BIST) 30 index. The experimental result indicates that our best proposed hybrid model has a successful forecast rate of 75%, which is higher than the single ANN or GA forecasting models.

• 경영과정보연구
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• 제27권
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• pp.53-69
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• 2008

In this paper we analyse performance of value strategy and moving average method among the non-financial listed companies whose fiscal year ends at December in the Korean Stock Exchange between 1996 and 2005. And we analyse combination investment performance of value investment and moving average method. After the analysis objective enterprises divide with the value stock and the growth stock, in accordance with moving average method we divide ascending stock and descending stock. And we compose 6 portfolios with combination of value stock, growth stock, ascending stock and descending stock. Using the difference of investment performance of these portfolios, when fundamental analysis and technical analysis method all considering we measure investment performance. The major findings of this research are as follows: First, the value strategy of buying value stocks and selling growth stocks were effective in the long-term investment. Second, using the moving average method, technical analysis were effective in the case of the short-term investment. Third, the portfolios combined fundamental analysis and technical analysis were more effective than investment performance of technical analysis.

• Journal of the Korean Data and Information Science Society
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• 제18권4호
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• pp.945-953
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• 2007

In this paper, we propose a smoothing and thus noise-reducing method of data sequences for stock price prediction with hidden Markov models, HMMs. The suggested method just uses simple moving average. A proper average size is obtained from forecasting experiments with stock prices of bank sector of Korean Exchange. Forecasting method with HMM and moving average smoothing is compared with a conventional method.

• International journal of advanced smart convergence
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• 제4권2호
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• pp.46-53
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• 2015

This paper suggests a method of real time confidence interval estimation to detect abnormal states of sensor data. For real time confidence interval estimation, the mean square errors of the exponential smoothing method and moving average method, two of the time series analysis method, were compared, and the moving average method with less errors was applied. When the sensor data passes the bounds of the confidence interval estimation, the administrator is notified through alarms. As the suggested method is for real time anomaly detection in a ship, an Android terminal was adopted for better communication between the wireless sensor network and users. For safe navigation, an administrator can make decisions promptly and accurately upon emergency situation in a ship by referring to the anomaly detection information through real time confidence interval estimation.

• Journal of the Korean Data and Information Science Society
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• 제26권4호
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• pp.907-914
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• 2015

저출산으로 인한 학생수의 감소는 교육 행정 정책에 있어서 매우 많은 변화들을 예고하고 있다. 본 연구는 2032년까지 정확한 학생 (인구) 수를 예측하기 위하여 연령 진급률 혹은 학년 진급률을 이용한 학생 (인구) 수를 추계하는 방법을 제시하는데 목적이 있다. 비례법을 이용한 이동평균비례법과 가중비례이동평균법이 인구추계의 방법으로 제시되었다. 제시된 방법들의 측정오차들에 대한 평균과 표준편차들을 모의실험을 통하여 구하였다. 본 연구에서 제시된 가중비례이동평균법과 이동평균비례법의 예측결과들은 낮게 추정되는 현상이 나타나고 있다. 이를 보완하여 대구 경북 지역의 고등학교 3학년 학생수를 추계하였다.

• 풍력에너지저널
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• 제10권3호
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• pp.5-11
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• 2019

This paper suggests and compares two algorithms, a moving average filter method and a method developed by the National Renewable Energy Laboratory (NREL), to verify the yaw control algorithm characteristic to reduce yaw error for a wind turbine. A characteristic change for yaw movement in accordance with control parameter change that consists of each control method has been verified. Also, yaw simulations were performed using nacelle wind data measured from two areas with different turbulence intensities and the yaw movement data in each area was compared. These two algorithms and real data were compared by calculating mean absolute error (MSE) and the number of yawing (NY). As a result of the analysis, the MSE values were not significantly different between the two algorithms, but the algorithm proposed by the NREL was found to reduce yaw movement by up to 50 percent more than the moving average filter method.

• 한국환경과학회지
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• 제27권12호
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• pp.1227-1239
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• 2018

In this study, daily precipitation data and daily average temperature data of meteorological observatories in Daegu, Busan, Daejeon, Seoul, Mokpo, and Gwangju cities inland and offshore were analyzed by using moving average method. Were compared. Overall, summarizing changes in precipitation and temperature over the 24 seasons, precipitation and temperature in all six stations increased compared to the past 1960s. In the case of precipitation, precipitation increased at the end of July and early August, whereas precipitation in April, September and early October decreased. In the case of temperature, especially in February, the temperature increased, and in Mokpo, the temperature from August to December showed a general decline. Changes in precipitation and temperature due to seasons in the 24 seasons affect agriculture and our everyday life, and further research is needed to determine how these changes will affect agricultural water supply, crop growth and daily life. The results of this study can be useful.

• 말소리와 음성과학
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• 제2권4호
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• pp.145-151
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• 2010

In this paper, a robust feature compensation method is proposed for improving the performance of speech recognition. The proposed method is incorporated into the auto-regressive moving average (ARMA) based feature compensation. We employ variable weights for the ARMA filter according to the degree of speech activity, and pass the normalized cepstral sequence through the weighted ARMA filter. Additionally when normalizing the cepstral sequences in training, the cepstral means and variances are estimated from total training utterances. Experimental results show the proposed method significantly improves the speech recognition performance in the noisy and reverberant environments.

• International Journal of Air-Conditioning and Refrigeration
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• 제11권3호
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• pp.125-131
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• 2003

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. In this paper, two fault detection methods were considered. One is a generic neural network, and the other is an moving average neural network. In order to compare the performance of fault detection results from these methods, two different types of faults in an air-conditioning system were applied. These are the condenser 30% fouling and the evaporator fan 25% slowdown. Test results showed that the moving average neural network was more effective for the detection of partial faults in the air-conditioning system.

• 전기학회논문지
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• 제67권2호
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• pp.293-300
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• 2018

We propose a moving object extraction and tracking method for improvement of animal identification and tracking technology. First, we propose a method of merging separated moving objects into a moving object by using FCM (Fuzzy C-Means) clustering algorithm to solve the problem of moving object loss caused by moving object extraction process. In addition, we propose a method of extracting data from a moving object and a method of counting moving objects to determine the number of clusters in order to satisfy the conditions for performing FCM clustering algorithm. Then, we propose a method to continuously track merged moving objects. In the proposed method, color histograms are extracted from feature information of each moving object, and the histograms are continuously accumulated so as not to react sensitively to noise or changes, and the average is obtained and stored. Thereafter, when a plurality of moving objects are overlapped and separated, the stored color histogram is compared with each other to correctly recognize each moving object. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.