• Journal of the Korean Operations Research and Management Science Society
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• v.12 no.1
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• pp.19-26
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• 1987

In analyzing time series, estimating the level or the current mean of the process plays an important role in understanding its structure and in being able to make forecasts. The studies the class of time series models where the level of the process is assumed to follow a random walk and the deviation from the level follow an ARMA process. The estimation and forecasting problem in a Bayesian framework and uses the Kalman filter to obtain forecasts based on estimates of level. In the analysis of time series, we usually make the assumption that the time series is generated by one model. However, in many situations the time series undergoes a structural change at one point in time. For example there may be a change in the distribution of random variables or in parameter values. Another example occurs when the level of the process changes abruptly at one period. In order to study such problems, the assumption that level follows a random walk process is relaxed to include a major level change at a particular point in time. The major level change is detected by examining the likelihood raio under a null hypothesis of no change and an alternative hypothesis of a major level change. The author proposes a method for estimation the size of the level change by adding one state variable to the state space model of the original Kalman filter. Detailed theoretical and numerical results are obtained for th first order autoregressive process wirth level changes.

• Journal of Environmental Science International
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• v.9 no.1
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• pp.19-25
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• 2000

As the tideland reclamation is done on a large scale these days, construction work is active in the coastal areas. Facilities in the coastal areas must be built with the tide characteristics taken into consideration. Thus the tide characteristics affect the overall reclamation plan. The analysis of the tide data boils down to a harmonic analysis of the hourly changes of long-term tide data and extraction of unharmonic coefficients from the results. Since considerable amount of tide data of the West Coast are available, the existing data can be collected and can be used to obtain the temporal changes of the tide by being fitted into the tide prediction model. The goal of this thesis lies in assessing whether the mean sea level used in the field agrees with the analysis results from the long-term observation data obtained with their homogeneity guaranteed. To achieve this goal, the research was conducted as follows. First the present conditions of the observation stations, the land level standard, and the sea level standard were analyzed to set up a time series model formula for representing them. To secure the homogeneity of the time series, each component was separated. Lastly the mean sea level used in the field was assessed based on the results obtained form the analysis of the time series.

• Journal of agriculture & life science
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• v.45 no.5
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• pp.115-126
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• 2011

This paper estimates the number of Korean cattle using time-series ARIMA model. This study classifies the structure of the number of cattle into six indexes to reflect the characteristics of cattle. This study apply ARIMA model to these six indexes according to Box-Jenkins procedure to identify, estimate and predict. The rates of slaughter for aged female and aged male cow is analyzed as non-stationary time series which has unit roots and other 4 indexes is analyzed as stationary time series. The differencing is applied to get rid of non-stationarity for the non-stationary time series. The results show that the number of cattle will be reduced from 2012 as a higher point and rebounded from 2018 as a lower point.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.183-189
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• 2024

In order to effectively predict time series data, this study proposed a hybrid prediction model that decomposes the data into trend, seasonality, and residual components using Seasonal-Trend Decomposition on Loess, and then applies ARIMA to the trend component, Fourier Series Regression to the seasonality component, and XGBoost to the remaining components. In addition, performance comparison experiments including ARIMA, XGBoost, LSTM, EMD-ARIMA, and CEEMDAN-LSTM models were conducted to evaluate the prediction performance of each model. The experimental results show that the proposed hybrid model outperforms the existing single models with the best performance indicator values in MAPE(3.8%), MAAPE(3.5%), and RMSE(0.35) metrics.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.87-96
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• 2000

Nonlinear time series prediction is derived and compared between statistic of modeling and neural network method. In particular mean squared errors of predication are obtained in generalized random coefficient model and generalized autoregressive conditional heteroscedastic model and compared with them by neural network forecasting.

• 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 Mechanical Science and Technology
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• v.17 no.11
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• pp.1608-1615
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• 2003

This paper studies on the linearization of a looper system in hot strip mills, that plays an important role in regulating a strip tension or a strip width. Nonlinear dynamic equations of the looper system are analytically linearized by a static feedback linearization algorithm with a compensator. The proposed linear model of the looper is validated by a comparison with a linear model using Taylor's series. It is shown that the linear model by static feedback well describes nonlinearities of the looper system than one using Taylor's series. Furthermore, it is shown from the design of an ILQ controller that the linear model by static feedback is very useful in designing a linear controller of the looper system.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.14-19
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• 2008

The paper presents an intelligent time series model to predict uncertain electricity market price in the deregulated industry environment. Since the price of electricity in a deregulated market is very volatile, it is difficult to estimate an accurate market price using historically observed data. The parameter of an intelligent time series model is obtained based on the simultaneous perturbation stochastic approximation (SPSA). The SPSA is flexible to use in high dimensional systems. Since prediction models have their modeling error, an error compensator is developed as compensation. The SPSA based intelligent model is applied to predict the electricity market price in the Pennsylvania-New Jersey-Maryland (PJM) electricity market.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.99-101
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• 2006

Auto-Cross Correlated time series (ACTS) model was constructed by using the backpropagation neural network. The performance of ACTS model was evaluated with sensor information collected from a large volume, industrial plasma-enhanced chemical vapor deposition system. A total of 18 sensor information were collected. The effect of inclusion of past and future information were examined. For all but three sensor information with a large data variance demonstrated a prediction error less than 3%. By integrating ACTS model into equipment software, process quality can be more stringently monitored while improving device throughput.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.102-104
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• 2006

Auto-Correlated time series (ATS) model was constructed by using the backpropagation neural network. The performance of ATS model was evaluated with sensor information collected from a large volume, industrial plasma-enhanced chemical vapor deposition system. A total of 18 sensor information were collected. The effect of inclusion of past and future information were examined. For all but three sensor information with a large data variance demonstrated a prediction error less than 4%. By integrating ATS model into equipment software, process quality can be more stringently monitored while improving device throughput.