• 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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• 제17권1호
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• pp.27-37
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• 2010

This article concerns the forecasting in binomial AR(p) models which is proposed by Wei$\ss$ (2009b) for time series of binomial counts. Our method extends to binomial AR(p) models a recent result by Jung and Tremayne (2006) for integer-valued autoregressive model of second order, INAR(2), with simple Poisson innovations. Forecasts are produced by conditional median which gives 'coherent' forecasts, and we estimate the forecast distributions of future values of binomial AR(p) models by means of a Monte Carlo method allowing for parameter uncertainty. Model parameters are estimated by the method of moments and estimated standard errors are calculated by means of block of block bootstrap. The method is fitted to log data set used in Wei$\ss$ (2009b).

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2000년도 하계종합학술대회논문집
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• pp.289-292
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• 2000

선형 자귀회귀(AR) 모델을 근거로한 HRV 파워 스펙트럼해석은 비침습적으로 자율신경의 반응을 정량화하는데 폭넓게 사용된다. 본 연구는 단구간(2분 미만)의 심전도와 맥파 신호로부터 시계열 HRV의 파워스펙트럼을 추정한다. 시계열은 정상인을 대상으로 검출한 심전도와 맥파신호의 특징점 시간간격(RRI, PPI)으로부터 구하였다. 발생된 시계열은 다항식 보간법에 의해 AR모델에 적합하게 재구성하였으며, AR모델 계수는 Burg법에 의해 계산하였다. AR모델을 적용한 단구간의 심전도와 맥파의 심박변동에 대한 파워스펙트럼밀도는 저주파수(LF)와 고주파수(HF)에서 매끄러운 스펙트럼 파워를 나타내고 있다. 또한 동일한 피험자의 심전도와 맥파의 파워스펙트럼밀도를 비교한 결과 동일한 모양을 나타내었다.

• Journal of the Korean Statistical Society
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• 제31권2호
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• pp.237-250
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• 2002

Motivated by Cook's (1986) assessment of local influence by investigating the curvature of a surface associated with the overall discrepancy measure, this paper extends this idea to the linear regression model with AR(p) disturbances. Diagnostic for the linear regression models with AR(p) disturbances are discussed when simultaneous perturbations of the response vector are allowed. For the derived criterion, numerical studies demonstrate routine application of this work.

• Journal of the Korean Statistical Society
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• 제31권1호
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• pp.121-128
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• 2002

In this paper we provide a new proof of the asymptotic distributions of LAD estimators using the martingale limit theorem and show the efficiency of LAD estimators in a stationary AR(1) model setting.

• 천문학회지
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• 제50권2호
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• pp.21-27
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• 2017

The autoregressive method provides a univariate procedure to predict the future sunspot number (SSN) based on past record. The strength of this method lies in the possibility that from past data it yields the SSN in the future as a function of time. On the other hand, its major limitation comes from the intrinsic complexity of solar magnetic activity that may deviate from the linear stationary process assumption that is the basis of the autoregressive model. By analyzing the residual errors produced by the method, we have obtained the following conclusions: (1) the optimal duration of the past time for the forecast is found to be 8.5 years; (2) the standard error increases with prediction horizon and the errors are mostly systematic ones resulting from the incompleteness of the autoregressive model; (3) there is a tendency that the predicted value is underestimated in the activity rising phase, while it is overestimated in the declining phase; (5) the model prediction of a new Solar Cycle is fairly good when it is similar to the previous one, but is bad when the new cycle is much different from the previous one; (6) a reasonably good prediction of a new cycle can be made using the AR model 1.5 years after the start of the cycle. In addition, we predict the next cycle (Solar Cycle 25) will reach the peak in 2024 at the activity level similar to the current cycle.

• 응용통계연구
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• 제20권1호
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• pp.103-115
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• 2007

본 논문에서는 1차 자기회귀모형에서 자기회귀계수에 대한 여러 가지 추정량들의 분포함수에 대한 근사 방법에 대해 연구하였다. 자기회귀계수의 여러 추정량들을 이차형식의 관점에서 이해하고, Na와 Kim(2005)에 의한 안장점근사의 결과를 이용한 새로운 근사법을 제시하였다. 이 방법은 정규근사를 비롯한 기존의 근사법과는 달리 추정량에 대한 근사분포의 유도과정이 불필요하며, 소표본은 물론 통계적 추론의 주요 관심영역에서의 근사정도가 매우 뛰어난 장점을 가지고 있다. 모의실험을 통해 Edgeworth 근사를 비롯한 기존의 여러 근사법보다 효율이 뛰어남을 확인하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.437-437
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• 2022

The prediction of dam inflow rate is crucial for the management of the largest multi-purpose dam in South Korea, the Soyang Dam. The main issue associated with the management of water resources is the stochastic nature of the reservoir inflow leading to an increase in uncertainty associated with the inflow prediction. The Autoregressive (AR) model is commonly used to provide the simulation and forecast of hydrometeorological data. However, because its estimation is based solely on the time-series data, it has the disadvantage of being unable to account for external variables such as climate information. This study proposes the use of the Autoregressive Exogenous Stochastic Volatility (ARXSV) model within a Bayesian modeling framework for increased predictability of the monthly dam inflow by addressing the exogenous and stochastic factors. This study analyzes 45 years of hydrological input data of the Soyang Dam from the year 1974 to 2019. The result of this study will be beneficial to strengthen the potential use of data-driven models for accurate inflow predictions and better reservoir management.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2012년도 춘계학술대회
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• pp.157-159
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• 2012

바람이나 파도에 의해서 해상 부유체의 진동이 발생하는데, 이러한 진동은 부유체와 부유체에 접안시킨 이동체 사이에 접촉을 야기하기 때문에 부유체나 이동체에 손상이 발생한다. 이 논문에서는 AR(Autoregressive) 모델과 ARX(AR with eXogenous) 모델을 이용하여 부유체에 발생하는 진동 모델링에 관해서 기술한다. 연구 방법은, 기준 잡음신호를 생성하여 기준 모델 계수를 구하고, 모의 충격신호를 생성하여 충격 모델 계수를 구한 후, 충격 신호만을 추출하여 가해진 충격의 정도를 분석 평가하였다. 본 연구는 향후 요트와 요트계류장의 안전확보 시스템 개발을 위한 기초 연구로 활용할 예정이다.

• 한국경영과학회지
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• 제26권1호
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• pp.109-116
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• 2001

The detection of corporate failures is a subject that has been particularly amenable to cross-sectional financial ratio analysis. In most of firms, however, the financial data are available over past years. Because of this, a model utilizing these longitudinal data could provide useful information on the prediction of bankruptcy. To correctly reflect the longitudinal and firm-specific data, the generalized linear model with assuming the first order AR(autoregressive) process is proposed. The method is motivated by the clinical research that several characteristics are measured repeatedly from individual over the time. The model is compared with several other predictive models to evaluate the performance. By using the financial data from manufacturing corporations in the Korea Stock Exchange (KSE) list, we will discuss some experiences learned from the procedure of sampling scheme, variable transformation, imputation, variable selection, and model evaluation. Finally, implications of the model with repeated measurement and future direction of research will be discussed.