데이터셋 상세
미국
Modeling of non-stationary autoregressive alpha-stable processe
In the literature, impulsive signals are mostly modeled by symmetric alpha-stable processes. To represent their temporal dependencies, usually autoregressive models with time-invariant coefficients are utilized. We propose a general sequential Bayesian modeling methodology where both unknown autoregressive coefficients and distribution parameters can be estimated successfully, even when they are time-varying. In contrast to most work in the literature on signal processing with alpha-stable distributions, our work is general and models also skewed alpha-stable processes. Successful performance of our method is demonstrated by computer simulations. We support our empirical results by providing posterior Cramer–Rao lower bounds. The proposed method is also tested on a practical application where seismic data events are modeled.
데이터 정보
연관 데이터
Scripts, data and plotting for "Simplified algorithms for adaptive experiment design in parameter estimation" v.2
공공데이터포털
Examples of adaptive measurement protocols using optimal Bayesian experiment design. This dataset supports "Simplified algorithms for adaptive experiment design in parameter estimation", arXiv 2202.08344 and submitted to Physical Review Applied. The calculations use python package optbayesexpt, which is available from https://github.com/usnistgov/optbayesexpt. The software applies to measurements of parameters in nonlinear parametric models. In the adaptive protocol, Incoming data influences parameter distributions via Bayesian inference and the parameter distribution influences predictions of the impact of future measurements.
Market Risk Scenarios
공공데이터포털
The FHFA methodology to construct a historically-based shock for a given interest rate is to measure the absolute change at each term point on the corresponding yield curve over a six-month horizon, and then impose that absolute change on the current measure of that yield curve. However, because the current rate environment may differ significantly from the historical rate environment, imposing the historical shock on the current rate can result in a shock scenario that is implausible. Implausible shock scenarios include any that contain negative values for interest rates, and those where the resulting spread between any two different interest rates is inconsistent with the historically observed spread. To ensure that the resulting shock scenarios are plausible, FHFA uses a technique known as parsimonious factorization to represent each yield curve as a five-factor equation, and then applies measures of the shock made in parameter space to the current yield curve parameters to determine the shocked yield curve. This approach facilitates a straightforward means to impose constraints on generating the shock scenario that ensure its plausibility.
환경부 한강홍수통제소 AI홍수예측결과 RMSE평가정보 AI홍수예측결과 RMSE 평가 정보
공공데이터포털
본 자료는 AI 수위 예측 결과 정확도 평가는 AIFRMSE 지표를 사용하며, RMSE 성능 평가를 통해 예측 지점 코드, 평가 정보와 10분 단위로 360분(6시간)까지의 예측 데이터를 분석한다. 이 데이터는 AI 홍수 예보 결과와 실제 관측값 간 차이를 평균제곱근 오차 방식으로 계산하여 예측 정확도를 평가한다. 본 자료는 업로드 용량 제한으로 샘플 자료 100개만 제공하며 전체 자료 요청은 담당에게 문의. 담당자: 기후에너지환경부 한강홍수통제소 이민호 연구사 02-590-9976
목록