Results 81 to 90 of about 4,031 (246)

A Comparison of Realized Measures of Integrated Volatility: Price Duration‐ vs. Return‐Based Approaches

Journal of Forecasting, EarlyView.
ABSTRACT We study the accuracy of a variety of parametric price duration‐based realized variance estimators constructed via various financial duration models and compare their forecasting performance with the performance of various nonparametric return‐based realized variance estimators.
Björn Schulte‐Tillmann, Mawuli Segnon, Timo Wiedemann   +2 more
wiley   +1 more source

Simplicial nonnegative matrix factorization

The 2013 RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF), 2013
Nonnegative matrix factorization (NMF) plays a crucial role in machine learning and data mining, especially for dimension reduction and component analysis. It is employed widely in different fields such as information retrieval, image processing, etc. After a decade of fast development, severe limitations still remained in NMFs methods including high ...
null Duy Khuong Nguyen, null Khoat Than, null Tu Bao Ho   +2 more
openaire   +1 more source

Forecasting Count Data With Varying Dispersion: A Latent‐Variable Approach

Journal of Forecasting, EarlyView.
ABSTRACT Count data, such as product sales and disease case counts, are common in business forecasting and many areas of science. Although the Poisson distribution is the best known model for such data, its use is severely limited by its assumption that the dispersion is a fixed function of the mean, which rarely holds in real‐world scenarios.
Easton Huch, Candace Berrett, Mason Ferlic, Kimberly F. Sellers   +3 more
wiley   +1 more source

Face Recognition Based on Wavelet Kernel Non-Negative Matrix Factorization

Cybernetics and Information Technologies, 2014
In this paper a novel face recognition algorithm, based on wavelet kernel non-negative matrix factorization (WKNMF), is proposed. By utilizing features from multi-resolution analysis, the nonlinear mapping capability of kernel nonnegative matrix ...
Bai, Lin, Li Yanbo, Hui Meng
doaj   +1 more source

Forecasting House Prices: The Role of Market Interconnectedness

Journal of Forecasting, EarlyView.
ABSTRACT While the existing research uncovers interconnections between various housing markets, it largely ignores the question of whether such linkages can improve house price predictions. To address this issue, we proceed in two steps. First, we forecast disaggregated house price growth rates from Australia and China to determine whether ...
Zac Chen, George Milunovich, Shuping Shi, Ben Wang   +3 more
wiley   +1 more source

Intraday Functional PCA Forecasting of Cryptocurrency Returns

Journal of Forecasting, EarlyView.
ABSTRACT We study the functional PCA (FPCA) forecasting method in application to functions of intraday returns on Bitcoin. We show that improved interval forecasts of future return functions are obtained when the conditional heteroscedasticity of return functions is taken into account.
Joann Jasiak, Cheng Zhong
wiley   +1 more source

Joint Estimation and Bandwidth Selection in Partially Parametric Models

Journal of Applied Econometrics, EarlyView.
ABSTRACT We propose a single‐step approach to estimating a model with both a known nonlinear parametric component and an unknown nonparametric component. We study the large sample behavior of a simultaneous optimization routine that estimates both the parameter vector of the parametric component and the bandwidth vector used to smooth the unknown ...
Daniel J. Henderson, Nadine McCloud, Christopher F. Parmeter   +2 more
wiley   +1 more source

Imperfect Synthetic Controls

Journal of Applied Econometrics, EarlyView.
ABSTRACT The synthetic control method assumes the existence of a perfect synthetic control, which cannot exist if the outcomes are functions of transitory shocks with nonzero asymptotic variance and may not exist even in expectation for the treated unit. This paper first shows the benefits of estimating synthetic controls for all units.
David Powell
wiley   +1 more source

Predicting Infrared Optical Properties of Materials Using Machine Learning Interatomic Potentials

Materials Genome Engineering Advances, EarlyView.
This work proposes a new fast computing framework for infrared reflectance spectra, MTP‐FIRE, based on machine learning potential, which can achieve the same accuracy as the existing first‐principles calculation, but can be two orders of magnitude faster on average.
Lianduan Zeng, Xiao Zhou, Xinxi Lu, Li Huang, Lan Yang, Lihao Wang, Gang Liu, Zhongyang Wang, Tongxiang Fan   +8 more
wiley   +1 more source

Semantic Intelligence in Metallurgy: A Dual‐Stage Language Model Framework for Processing‐Aware Magnesium Alloy Design

Materials Genome Engineering Advances, EarlyView.
This study introduces a dual‐stage language model framework that extracts processing information from scientific literature and integrates it with thermodynamic data for magnesium alloy design. By combining semantic intelligence with quantitative modeling, the approach enables processing‐aware prediction of mechanical and thermal properties, bridging ...
Ziliang Lu, Ishwar Kapoor, Takeru Araki, Lu Chen, Tao Song, Yixiang Li, Yang Su, Xiaoqin Zeng, Leyun Wang   +8 more
wiley   +1 more source