• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.857-860
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• 1993

In this paper, we apply the self generating neuro fuzzy model (SGNFM) to the dimension analysis of the chaotic time series. Firstly, we formulate a nonlinear time series identification problem with nonlinear autoregressive (NARMAX) model. Secondly, we propose an identification algorithm using SGNFM. We apply this method to the estimation of embedding dimension for chaotic time series, since the embedding dimension plays an essential role for the identification and the prediction of chaotic time series. In this estimation method, identification problems with gradually increasing embedding dimension are solved, and the identified result is used for computing correlation coefficients between the predicted time series and the observed one. We apply this method to the dimension estimation of a chaotic pulsation in a finger's capillary vessels.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.185-192
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• 2022

Forecasting and time series modelling plays a vital role in the data analysis process. Time Series is widely used in analytics & data science. Forecasting stock prices is a popular and important topic in financial and academic studies. A stock market is an unregulated place for forecasting due to the absence of essential rules for estimating or predicting a stock price in the stock market. Therefore, predicting stock prices is a time-series problem and challenging. Machine learning has many methods and applications instrumental in implementing stock price forecasting, such as technical analysis, fundamental analysis, time series analysis, statistical analysis. This paper will discuss implementing the stock price, forecasting, and research using prophet and LSTM models. This process and task are very complex and involve uncertainty. Although the stock price never is predicted due to its ambiguous field, this paper aims to apply the concept of forecasting and data analysis to predict stocks.

• Korean Management Science Review
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• v.28 no.1
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• pp.53-60
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• 2011

Time series analysis methods have been traditionally used in stock price prediction. However, most of the existing methods represent some methodological limitations in reflecting influence from external factors that affect the fluctuation of stock prices, such as oil prices, exchange rates, money interest rates, and the stock price indexes of other countries. To overcome the limitations, we propose a network based method incorporating the relations between the individual company stock prices and the external factors by using a graph-based semi-supervised learning algorithm. For verifying the significance of the proposed method, it was applied to the prediction problems of company stock prices listed in the KOSPI from January 2007 to August 2008.

• Journal of the Korean Statistical Society
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• v.35 no.1
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• pp.37-47
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• 2006

Recently, as a result of the growing interest in modeling stationary processes with discrete marginal distributions, several models for integer valued time series have been proposed in the literature. One of these models is the integer-valued autoregressive (INAR) models. However, when modeling with integer-valued autoregressive processes, the distributional properties of forecasts have been not yet discovered due to the difficulty in handling the Steutal Van Ham thinning operator 'o' (Steutal and van Ham, 1979). In this study, we derive the mean squared error of h-step-ahead prediction from a Poisson INAR(1) process, reflecting the effect of the variability of parameter estimates in the prediction mean squared error.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.26 no.4
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• pp.101-109
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• 2018

In order to predict the future needs of the aircraft repair parts, each military group develops and applies various techniques to their characteristics. However, the aircraft and the equipped weapon systems are becoming increasingly advanced, and there is a problem in improving the hit rate by applying the existing demand prediction technique due to the change of the aircraft condition according to the long term operation of the aircraft. In this study, we propose a new prediction model based on the conventional time-series analysis technique to improve the prediction accuracy of aircraft repair parts by using machine learning model. And we show the most effective predictive method by demonstrating the change of hit rate based on actual data.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.357-362
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• 2012

We have compared and predicted for non-linear time series data which are real data having different variences using GRCA(1) model and neural network method. In particular, using Korea Composite Stock Price Index rate, mean square errors of prediction are obtained in genaralized random coefficient autoregressive model and neural network method. Neural network method prove to be better in short-term forecasting, however GRCA(1) model perform well in long-term forecasting.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.799-803
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• 2007

In this paper, we propose a new fuzzy membership function for FSVM(Fuzzy Support Vector Machines). We apply a fuzzy membership to each input point of SVM and reformulate SVM into fuzzy SVM (FSVM) such that different input points can make different contributions to the learning of decision surface. The proposed method enhances the SVM in reducing the effect of outliers and noises in data points. This paper compares classification and estimated performance of SVM, FSVM(1), and FSVM(2) model that are getting into the spotlight in time series prediction.

• Journal of Urban Science
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• v.7 no.1
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• pp.45-54
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• 2018

The purpose of this study is to find the factors that reduce prediction error in traffic volume using highway traffic volume data. The ARIMA model was used to predict the day, and it was confirmed that weekday and weekly characteristics were distinguished by prediction error. The forecasting results showed that weekday characteristics were prominent on Tuesdays, Wednesdays, and Thursdays, and forecast errors including MAPE and MAE on Sunday were about 15% points and about 10 points higher than weekday characteristics. Also, on Friday, the forecast error was high on weekdays, similar to Sunday's forecast error, unlike Tuesday, Wednesday, and Thursday, which had weekday characteristics. Therefore, when forecasting the time series belonging to Friday, it should be regarded as a weekly characteristic having characteristics similar to weekend rather than considering as weekday.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.32 no.6
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• pp.191-198
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• 1983

This paper describes the aplication of different model which can be used for short-term load prediction. The model is based on Bohlin's approach to first develop a load profile model representing the nominal load component and the Box-Jenkins approach is used to predict residuals. An on-line algorithm using Kalman Filter and Time Series is implemented for and hour-ahead prediction. In the Kalman Filter system equation and measurement equation were fixed and parameters of Time Series were varied week after week. A set of data for Korea Electric Power Corporation from April to June 1981 was used for the evaluation of the model. As the result of this simulation 1.2% rms error was acquired.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.98-103
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• 2021

Recently, as the need for food resources has increased both domestically and internationally, support for the agricultural sector for stable food supply and demand is expanding in Korea. However, according to recent media articles, the biggest problem in rural communities is the unstable profit structure. In addition, in order to confirm the profit structure, profit forecast data must be clearly prepared, but there is a lack of auxiliary data for farmers or future returnees to predict farm income. Therefore, in this paper we analyzed data over the past 15 years through time series analysis and proposes an artificial intelligence farm income prediction algorithm that can predict farm household income in the future. If the proposed algorithm is used, it is expected that it can be used as auxiliary data to predict farm profits.