• Communications for Statistical Applications and Methods
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• 제17권3호
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• pp.441-450
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• 2010

In this paper we consider the problem of forecasting binomial time series, modelled by the binomial autoregressive model. This paper considers proposed by McKenzie (1985) and is extended to a higher order by $Wei{\ss}$(2009). Since the binomial autoregressive model is a Markov chain, we can apply the earlier work of Bu and McCabe (2008) for integer valued autoregressive(INAR) model to the binomial autoregressive model. We will discuss how to compute the h-step-ahead forecast of the conditional probabilities of $X_{T+h}$ when T periods are used in fitting. Then we obtain the maximum likelihood estimator of binomial autoregressive model and use it to derive the maximum likelihood estimator of the h-step-ahead forecast of the conditional probabilities of $X_{T+h}$. The methodology is illustrated by applying it to a data set previously analyzed by $Wei{\ss}$(2009).

• Communications for Statistical Applications and Methods
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• 제25권6호
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• pp.659-671
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• 2018

In this study, we consider the extension of the heterogeneous autoregressive (HAR) model for realized volatility by incorporating a neural network (NN) structure. Since HAR is a linear model, we expect that adding a neural network term would explain the delicate nonlinearity of the realized volatility. Three neural network-based HAR models, namely HAR-NN, $HAR({\infty})-NN$, and HAR-AR(22)-NN are considered with performance measured by evaluating out-of-sample forecasting errors. The results of the study show that HAR-NN provides a slightly wider interval than traditional HAR as well as shows more peaks and valleys on the turning points. It implies that the HAR-NN model can capture sharper changes due to higher volatility than the traditional HAR model. The HAR-NN model for prediction interval is therefore recommended to account for higher volatility in the stock market. An empirical analysis on the multinational realized volatility of stock indexes shows that the HAR-NN that adds daily, weekly, and monthly volatility averages to the neural network model exhibits the best performance.

• Communications for Statistical Applications and Methods
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• 제29권1호
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• pp.53-64
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• 2022

High dimensional time series is gaining considerable attention in recent years. The sparse vector heterogeneous autoregressive (VHAR) model proposed by Baek and Park (2020) uses adaptive lasso and debiasing procedure in estimation, and showed superb forecasting performance in realized volatilities. This paper extends the sparse VHAR model by considering non-convex penalties such as SCAD and MCP for possible bias reduction from their penalty design. Finite sample performances of three estimation methods are compared through Monte Carlo simulation. Our study shows first that taking into cross-sectional correlations reduces bias. Second, nonconvex penalties performs better when the sample size is small. On the other hand, the adaptive lasso with debiasing performs well as sample size increases. Also, empirical analysis based on 20 multinational realized volatilities is provided.

• Journal of the Korean Data and Information Science Society
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• 제20권1호
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• pp.159-169
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• 2009

본 연구는 이동평균을 이용하여 2009년부터 2026년까지 대구 경북 (대구광역시와 경상북도) 지역의 고3학생수 (고등학교 3학년 학생 수)를 예측하고, 그 결과와 교육인적자원 통계서비스의 2005년부터 2007년까지의 고3학생수에 대한 예측들과 비교분석하였다. 그리고 고3학생수의 감소에 대하여 대구 경북의 전문대학을 포함한 대학들의 입학정원수와 관계를 분석하였다. 분석결과 2007년의 고3학생수를 기준으로 볼 때, 교육인적자원 통계시스템의 예측연도2007년 예측결과인 2017년에 입학생유치의 어려움은 피하더라도 2-3년 후인 2019년과 2020년에는 대구?경북의 입학생 유치에 심각한 어려움이 오는 것으로 예측이 된다.

• 천문학회보
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• 제37권2호
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• pp.123.1-123.1
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• 2012

We have compared near-real time Kp forecast models based on neural network (NN) and support vector machine (SVM) algorithms. We consider four models as follows: (1) a NN model using ACE solar wind data; (2) a SVM model using ACE solar wind data; (3) a NN model using ACE solar wind data and preliminary kp values from US ground-based magnetometers; (4) a SVM model using the same input data as model 3. For the comparison of these models, we estimate correlation coefficients and RMS errors between the observed Kp and the predicted Kp. As a result, we found that the model 3 is better than the other models. The values of correlation coefficients and RMS error of the model 3 are 0.93 and 0.48, respectively. For the forecast evaluation of models for geomagnetic storms ($Kp{\geq}6$), we present contingency tables and estimate statistical parameters such as probability of detection yes (PODy), false alarm ratio (FAR), bias, and critical success index (CSI). From a comparison of these statistical parameters, we found that the SVM models (model 2 and model 4) are better than the NN models (model 1 and model 3). The values of PODy and CSI of the model 4 are the highest among these models (PODy: 0.57 and CSI: 0.48). From these results, we suggest that the NN models are better than the SVM models for predicting Kp and the SVM models are better than the NN models for forecasting geomagnetic storms.

• 전기전자학회논문지
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• 제26권3호
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• pp.355-363
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• 2022

우리나라는 2050년 탄소중립을 목표로 신재생에너지 중심으로 에너지 공급원을 전환하고 확대하는 계획을 추진 중이다. 신재생에너지의 간헐적 특성으로 에너지 공급이 불안정성이 커짐에 따라 정확한 신재생에너지 발전량 예측의 중요성이 함께 커지고 있다. 이에 따라 정부는 신재생에너지를 집합화하여 관리하기 위한 소규모 전력중개시장을 개설하였고, 재생에너지 발전량 예측제도를 도입하여 예측정확도에 따라 정산금을 지급하는 제도를 시행 중이다. 본 논문에서는 우리나라 신재생에너지 전원의 대부분을 차지하는 태양광 발전에 대하여 통계적 및 인공지능 모형을 이용하여 예측모델을 구현하였으며, 각 모형의 예측정확도 결과를 비교 분석하였다. 비교 모델 중에서 CNN-LSTM(Convolutional Long Short-Term Memory Neural Networks) 모형이 가장 높은 성능을 가짐을 확인하였다. 예측정확도에 따른 예측제도 정산금 수익을 추정해보았고, 예측보유 기술 수준에 따라 수익 편차가 24% 정도 커질 수 있음을 확인하였다.

• Genomics & Informatics
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• 제21권4호
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• pp.50.1-50.9
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• 2023

Vaccine development is one of the key efforts to control the spread of coronavirus disease 2019 (COVID-19). However, it has become apparent that the immunity acquired through vaccination is not permanent, known as the waning effect. Therefore, monitoring the proportion of the population with immunity is essential to improve the forecasting of future waves of the pandemic. Despite this, the impact of the waning effect on forecasting accuracies has not been extensively studied. We proposed a method for the estimation of the effective immunity (EI) rate which represents the waning effect by integrating the second and booster doses of COVID-19 vaccines. The EI rate, with different periods to the onset of the waning effect, was incorporated into three statistical models and two machine learning models. Stringency Index, omicron variant BA.5 rate (BA.5 rate), booster shot rate (BSR), and the EI rate were used as covariates and the best covariate combination was selected using prediction error. Among the prediction results, Generalized Additive Model showed the best improvement (decreasing 86% test error) with the EI rate. Furthermore, we confirmed that South Korea's decision to recommend booster shots after 90 days is reasonable since the waning effect onsets 90 days after the last dose of vaccine which improves the prediction of confirmed cases and deaths. Substituting BSR with EI rate in statistical models not only results in better predictions but also makes it possible to forecast a potential wave and help the local community react proactively to a rapid increase in confirmed cases.

• 응용통계연구
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• 제24권1호
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• pp.1-10
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• 2011

본 연구는 포트폴리오 이론에 입각해 위험을 최소화하기 위한 투자의 국제적 분산 가능성에 대해 통계적으로 검정해 보았다. 국내외 주요 주식시장 간 동적 상호의존 관계와 구조변화를 검색하는 접근방식을 적용시켜 본 결과 아시아 외환위기에 따른 공통요인들의 존재로 인해 각 주식시장의 독자적 변동이 제약을 받아 투자의 다각화에 따른 수익이 제한되는 것으로 나타났다. 투자 다변화 여건이 조성되는 시기는 주식시장 간 동조화 현상이 약화된 이후로 판명되며, 검정결과는 당시 해외투자와 펀드판매의 증가 시기 및 시장성향의 현실을 그대로 반영한다.

• 한국도로학회논문집
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• 제15권3호
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• pp.93-101
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• 2013

PURPOSES : This study is to investigate the relationship of socioeconomic characteristics and road network structure with traffic growth patterns. The findings is to be used to tweak traffic forecast provided by traditional four step process using relevant socioeconomic and road network data. METHODS: Comprehensive statistical analysis is used to identify key explanatory variables using historical observations on traffic forecast, actual traffic counts and surrounding environments. Based on statistical results, a multiple regression model is developed to predict the effects of socioeconomic and road network attributes on traffic growth patterns. The validation of the proposed model is also performed using a different set of historical data. RESULTS : The statistical analysis results indicate that several socioeconomic characteristics and road network structure cleary affect the tendency of over- and under-estimation of road traffics. Among them, land use is a key factor which is revealed by a factor that traffic forecast for urban road tends to be under-estimated while rural road traffic prediction is generally over-estimated. The model application suggests that tweaking the traffic forecast using the proposed model can reduce the discrepancies between the predicted and actual traffic counts from 30.4% to 21.9%. CONCLUSIONS : Prediction of road traffic growth patterns based on surrounding socioeconomic and road network attributes can help develop the optimal strategy of road construction plan by enhancing reliability of traffic forecast as well as tendency of traffic growth.

• Journal of the Korean Data and Information Science Society
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• 제21권3호
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• pp.503-511
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• 2010

본 연구의 목적은 연령진급률 혹은 학년진급률을 추정하기 위한 방법으로 비례법을 사용한 이동평균법에 의한 알고리즘을 제시하는데 있다. 학년진급률에 따른 학생 수 추계방법으로, 이동평균법과 비례이동평균법에 의한 추정방법을 제시하고, 2027년까지의 서울시의 고3학생 수를 추정하여, 한국교육개발원의 2005년, 2006년, 2007년의 로지스틱함수 추정에 의한 고3학생 수 예측결과와 비교 분석하였다. 본 연구의 결과 출생아수의 분포와 비교하여 볼 때, 본 연구에서 제시된 비례이동평균법과 이동평균법의 예측결과가 한국교육개발원의 2005년, 2006년, 2007년의 고3학생 수의 예측결과보다 더 신뢰성이 있는 것으로 나타난다.