• Journal of Korean Society on Water Environment
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• v.20 no.3
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• pp.223-230
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• 2004

The QUAL2E and Box-Jenkins time series model were applied to the Miho river, a main tributary of the Geum river, to predict water quality. The models are widely used to predict water quality in rivers and watersheds because of its accuracy. As results of the study, we concluded as follows: Pollutant loadings in upper stream of Miho river were determined to 57,811 kgBOD/d, 19,350 kgTN/d, and 5,013 kgTP/d. The loading of TN in Mushim river was 19,450 kgTN/d, respectively. As the mass loadings were compared with pollutant sources, it concluded that the farming livestock contributed highly to mass emissions of BOD and TP and the population contributed to TN mass loading. The observed water quality values were applied to the models to verify and the models were used to predict the water quality. The QUAL2E Model predicted the concentrations of DO, BOD, TN and TP with high accuracy, but not for E-Coli. The Box-Jenkins time series model also showed high prediction for DO, BOD and TN. However, the concentrations of TP and E-Coli were poorly predicted. The result shows that the QUAL2E model is more applicable in Miho basin for prediction of water quality compared to Box-Jenkins time series model.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.731-739
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• 2000

Conditional least square estimators for the parameters of he NLAR(p) time series models are obtained. it is also shown that these estimators are consistent and asymptotically normal.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2B
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• pp.163-171
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• 2009

Classical linear models have been generally used to analyze and forecast hydrologic time series. However, there is growing evidence of nonlinear structure in natural phenomena and hydrologic time series associated with their patterns and fluctuations. Therefore, the classical linear techniques for time series analysis and forecasting may not be appropriate for nonlinear processes. In recent, the BDS (Brock-Dechert-Scheinkman) statistic instead of conventional techniques has been used for detecting nonlinearity of time series. The BDS statistic was derived from the statistical properties of the correlation integral which is used to analyze chaotic system and has been effectively used for distinguishing nonlinear structure in dynamic system from random structures. DVS (Deterministic Versus Stochastic) algorithm has been used for detecting chaos and stochastic systems and for forecasting of chaotic system. This study showed the DVS algorithm can be also used for detecting nonlinearity of the time series. In this study, the stochastic and hydrologic time series are analyzed to detect their nonlinearity. The linear and nonlinear stochastic time series generated from ARMA and TAR (Threshold Auto Regressive) models, a daily streamflow at St. Johns river near Cocoa, Florida, USA and Great Salt Lake Volume (GSL) data, Utah, USA are analyzed, daily inflow series of Soyang dam and the results are compared. The results showed the BDS statistic is a powerful tool for distinguishing between linearity and nonlinearity of the time series and DVS plot can be also effectively used for distinguishing the nonlinearity of the time series.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.1005-1013
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• 2017

In this paper, we investigate the performances of time series models to forecast irradiance that consider weather variables such as temperature, humidity, cloud cover and Global Horizontal Irradiance. We first introduce the time series models and show that regression ARIMAX has the best performance with other models such as ARIMA and multiple regression models.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.487-493
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• 2015

Time series data sometimes show violation of normal assumptions. For cases where the assumption of normality is untenable, more exible models can be adopted to accommodate heavy tails. The exponential power distribution (EPD) is considered as possible candidate for errors of time series model that may show violation of normal assumption. Besides, the use of exible models for errors like EPD might be able to conduct the robust analysis. In this paper, we especially consider EPD as the exible distribution for errors of autoregressive models. Also, we represent this distribution as scale mixture of uniform and this form enables efficient Bayesian estimation via Markov chain Monte Carlo (MCMC) methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.1-10
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• 1999

In recent industry society, it is revealed that, as an increase in the use of automated manufacturing and process inspection technology, the data from mass production system exhibits some degrees of autocorrelation. The operation characteristics of traditional control charts developed under the independence assumption are adversely affected by the presence of serial correlation. Therefore, when autocorrelated construction contacted with time-series models explain, the time-series models are the Box-Jenkins forecast models which have been proposed as the best forecasting tool which allows for partitioning of variation into result from the autocorrelation structure and variation due to unusual but assignable causes. In this paper, for the AR(1) process of Box-Jenkins forecast models, when the constant term ξ are zero and different from zero, I want to analyze the sensitivity of (equation omitted), CUSUM and EWMA control chart for forecast residuals.

• Communications for Statistical Applications and Methods
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• v.27 no.2
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• pp.177-187
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• 2020

This article is concerned with the issue of forecasting and evaluation of threshold-asymmetric volatility models for time series of count data. In particular, threshold integer-valued models with conditional Poisson and conditional negative binomial distributions are highlighted. Based on the parametric bootstrap method, some evaluation measures are discussed in terms of one-step ahead forecasting. A parametric bootstrap procedure is explained from which directional measure, magnitude measure and expected cost of misclassification are discussed to evaluate competing models. The cholera data in Bangladesh from 1988 to 2016 is analyzed as a real application.

• Communications for Statistical Applications and Methods
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• v.28 no.4
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• pp.329-338
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• 2021

Deep learning methods have been developed, used in various fields, and they have shown outstanding performances in many cases. Many studies predicted a daily stock return, a classic example of time-series data, using deep learning methods. We also tried to apply deep learning methods to Korea's stock market data. We used Korea's stock market index (KOSPI) and several individual stocks to forecast daily returns and directions. We compared several deep learning models with other machine learning methods, including random forest and XGBoost. In regression, long short term memory (LSTM) and gated recurrent unit (GRU) models are better than other prediction models. For the classification applications, there is no clear winner. However, even the best deep learning models cannot predict significantly better than the simple base model. We believe that it is challenging to predict daily stock return data even if we use the latest deep learning methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.1-14
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• 2024

Time-series forecasting is extensively used in the actual world. Recent research has shown that Transformers with a self-attention mechanism at their core exhibit better performance when dealing with such problems. However, most of the existing Transformer models used for time series prediction use the traditional encoder-decoder architecture, which is complex and leads to low model processing efficiency, thus limiting the ability to mine deep time dependencies by increasing model depth. Secondly, the secondary computational complexity of the self-attention mechanism also increases computational overhead and reduces processing efficiency. To address these issues, the paper designs an efficient multi-layer attention-based time-series forecasting model. This model has the following characteristics: (i) It abandons the traditional encoder-decoder based Transformer architecture and constructs a time series prediction model based on multi-layer attention mechanism, improving the model's ability to mine deep time dependencies. (ii) A cross attention module based on cross attention mechanism was designed to enhance information exchange between historical and predictive sequences. (iii) Applying a recently proposed sparse attention mechanism to our model reduces computational overhead and improves processing efficiency. Experiments on multiple datasets have shown that our model can significantly increase the performance of current advanced Transformer methods in time series forecasting, including LogTrans, Reformer, and Informer.

• Journal of Korean Society of Forest Science
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• v.95 no.2
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• pp.203-208
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• 2006

This study compared the roundwood demand prediction accuracy of econometric and time-series models using Korean data. The roundwood was divided into softwood and hardwood by species. The econometric model of roundwood demand was specified with four explanatory variables; own price, substitute price, gross domestic product, dummy. The time-series model was specified with lagged endogenous variable. The dummy variable reflected the abrupt decrease in roundwood demand in the late 1990's in the case of softwood roundwood, and the boom of plywood export in the late 1970's in the case of hardwood roundwood. On the other hand, the prediction accuracy was estimated on the basis of Residual Mean Square Errors(RMSE). The results showed that the softwood roundwood demand prediction can be performed more accurately by econometric model than by time-series model. However, the hardwood roundwood demand prediction accuracy was similar in the case of using econometric and time-series model.