• Journal of the Korean Data and Information Science Society
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• v.20 no.3
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• pp.475-483
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• 2009

In this paper we introduced a class of nonlinear time series models to analyse KOSPI data. We introduce the Generalized Power-Transformation TGARCH (GPT-TGARCH) model and the model includes Zakoian (1993) and Li and Li (1996) models as the special cases. We showed the effectiveness and efficiency of the new model based on KOSPI data.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.3
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• pp.75-82
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• 2022

The purpose of the Consumer sentiment index survey is to determine the consumer's economic situation and consumption spending plan, and it is used as basic data for diagnosing economic phenomena and forecasting the future economic direction. The purpose of this paper is to analyze and predict the future Consumer sentiment index using the ARMA model based on the past consumer index. Consumer sentiment index is determined according to consumer trends, so it can reflect consumer realities. The consumer sentiment index is greatly influenced by economic indicators such as the base interest rate and consumer price index, as well as various external economic factors. If the consumer sentiment index, which fluctuates greatly due to consumer economic conditions, can be predicted, it will be useful information for households, businesses, and policy authorities. This study predicted the Consumer sentiment index for the next 3 years (36 months in total) by using time series analysis using the ARMA model. As a result of the analysis, it shows a characteristic of repeating an increase or a decrease every month according to the consumer trend. This study provides empirical results of prediction of Consumer sentiment index through statistical techniques, and has a contribution to raising the need for policy authorities to prepare flexible operating policies in line with economic trends.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.75-87
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• 2023

We designed to employ an Artificial Intelligence learning model to predict real estate prices and determine the reasons behind their changes, with the goal of using the results as a guide for policy. Numerous studies have already been conducted in an effort to develop a real estate price prediction model. The price prediction power of conventional time series analysis techniques (such as the widely-used ARIMA and VAR models for univariate time series analysis) and the more recently-discussed LSTM techniques is compared and analyzed in this study in order to forecast real estate prices. There is currently a period of rising volatility in the real estate market as a result of both internal and external factors. Predicting the movement of real estate values during times of heightened volatility is more challenging than it is during times of persistent general trends. According to the real estate market cycle, this study focuses on the three times of extreme volatility. It was established that the LSTM, VAR, and ARIMA models have strong predictive capacity by successfully forecasting the trading price index during a period of unusually high volatility. We explores potential synergies between the hybrid artificial intelligence learning model and the conventional statistical prediction model.

• Magazine of the Korean Society of Agricultural Engineers
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• v.34 no.1
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• pp.100-106
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• 1992

The objective of this study is to develop a real-time runoff forecasting model considering stochastic component. The model is composed of deterministic and stochastic components. Simplified tank model was selected as a deterministic runoff forecasting model. The time series of estimation residual resulting from the tank model simulation was analyzed and was best suited to the second-order autoregressive model. ARTANK model which combined the tank model with the autoregressive process was developed. And it was applied to a BANWEOL basin for validation. The simulation results showed a good agreement with the observed field data.

• Smart Structures and Systems
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• v.29 no.1
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• pp.105-116
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• 2022

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.488-497
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• 2010

8100 series Electric locomotive was imported from SIEMENS of Germany on 2001. 8100 series model is modified from BR152 model to be complied with national environment. After 8100 series Electric locomotive carried out main line test for long time, 8200 series Electric locomotive produced in 10 locomotives with improvement and supplement on some parts. the one of supplement is to install the HEP equipment. In paper, about total harmonic distortion of HEP equipment power, it compares the result of combination with the result of actual measurement. It checks whether the measurement meets the specification of design or not. And it describes international standard about total harmonic distortion of supply system.

• Journal of Korea Water Resources Association
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• v.51 no.2
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• pp.165-174
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• 2018

The model using time series data can be considered as a flood forecasting model of a small river due to its efficiency for model development and the advantage of rapid simulation for securing predicted time when reliable data are obtained. Transfer Function Noise (TFN) model has been applied hourly flood forecast in Italy, and UK since 1970s, while it has mainly been used for long-term simulations in daily or monthly basis in Korea. Recently, accumulating hydrological data with good quality have made it possible to simulate hourly flood prediction. The purpose of this study is to assess the TFN model applicability that can reflect exogenous variables by combining dynamic system and error term to reduce prediction error for tributary rivers. TFN model with hourly data had better results than result from Storage Function Model (SFM), according to the flood events. And it is expected to expand to similar sized streams in the future.

• Journal of KSNVE
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• v.9 no.1
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• pp.60-69
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• 1999

In the first paper of this research$^{(1)}$. a new time series method. directional ARMAX (dARMAX) model-based approach. was proposed for rotordynamics identification. The dARMAX processes complex-valued signals, utilizing the complex modal testing theory which enables the separation of the backward and forward modes in the two-sided frequency domain and makes effective modal parameter identification possible. to account for the dynamic characteristics inherent in rotating machinery. In this second part. an evaluation of its performance characteristics based on both simulated and experimental data is presented. Numerical simulations are carried out to show that the method. a complex time series method. successfully implements the complex modal testing in the time domain. and it is superior in nature to the conventional ARMAX and the frequency-domain methods in the estimation of the modal parameters for isotropic and weakly anisotropic rotor systems. Experiments are carried out to demonstrate the applicability and the effectiveness of the dARMAX model-based approach, following the proposed fitting strategy. for the rotordynamics identification.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.1025-1034
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• 2015

Traditional Shewhart control charts assume that the observations are independent over time. Current progress in measurement and data collection technology lead to the presence of autocorrelated process data that may affect poor performance in statistical process control. One of the most popular charts for autocorrelated data is to model a correlative structure with an appropriate time series model and apply control chart to the sequence of residuals. Model parameters are estimated by an in-control Phase I reference sample since they are usually unknown in practice. This paper deals with the effects of parameter estimation on Phase II control limits to monitor autocorrelated data.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.82-91
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• 2022

In this paper, time series prediction method of photovoltaic power is introduced using seasonal autoregressive integrated moving average (SARIMA). In order to obtain the best fitting model by a time series method in the absence of an environmental sensor, this research was used data below 50% of cloud cover. Three samples were extracted by time intervals from the raw data. After that, the best fitting models were derived from mean absolute percentage error (MAPE) with the minimum akaike information criterion (AIC) or beysian information criterion (BIC). They are SARIMA (1,0,0)(0,2,2)14, SARIMA (1,0,0)(0,2,2)28, SARIMA (2,0,3)(1,2,2)55. Generally parameter of model derived from BIC was lower than AIC. SARIMA (2,0,3)(1,2,2)55, unlike other models, was drawn by AIC. And the performance of models obtained by SARIMA was compared. MAPE value was affected by the seasonal period of the sample. It is estimated that long seasonal period samples include atmosphere irregularity. Consequently using 1 hour or 30 minutes interval sample is able to be helpful for prediction accuracy improvement.