Results 51 to 60 of about 26,074 (306)
Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models
Discrete Dynamics in Nature and Society, 2020 The aim of this paper is to forecast monthly crude oil price with a hierarchical shrinkage approach, which utilizes not only LASSO for predictor selection, but a hierarchical Bayesian method to determine whether constant coefficient (CC) or time-varying ...
Yuntong Liu, Yu Wei, Yi Liu, Wenjuan Li
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Multimodal Data‐Driven Microstructure Characterization
Advanced Engineering Materials, EarlyView.A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang +4 more
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Ekonomska Misao i Praksa, 2017 This paper investigates the concept and estimation of the output gap in transition economies, with special reference to the Czech Republic, Estonia and Kosovo.
Albulenë Kastrati +2 more
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Advanced Engineering Materials, EarlyView.Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran +6 more
wiley +1 more source
A Note on ‘What Drives Share Prices in the Middle East?’
The International Journal of Banking and Finance, 2008 There are several hypotheses suggesting that some properties of oil prices make it interesting to focus on the predictive ability of oil prices for stock returns.
Panos Priftakis, M. Ishaq Bhatti
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Tropical Agricultural Research, 2015 Forecasting a time series is generally done by using autoregressive integrated moving average (ARIMA) models. The main drawback of this technique is that the time series should be stationary. In reality, this assumption is rarely met.
N.K.K. Brintha +4 more
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Dynamic generalised additive models (DGAMs) for forecasting discrete ecological time series
Methods in Ecology and Evolution, 2023 Generalised additive models (GAMs) are increasingly popular tools for estimating smooth nonlinear relationships between predictors and response variables.
Nicholas J. Clark, Konstans Wells
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On the identification of multivariate correlated unobserved components models [PDF]
Economics Letters, 2016 This paper analyses identification for multivariate unobserved components models in which the innovations to trend and cycle are correlated. We address order and rank criteria as well as potential non-uniqueness of the reduced-form VARMA model. Identification is shown for lag lengths larger than one in case of a diagonal vector autoregressive cycle.
Trenkler, Carsten, Weber, Enzo
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Advanced Engineering Materials, EarlyView.This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer +4 more
wiley +1 more source
Measuring the Euro-Dollar Permanent Equilibrium Exchange Rate using the Unobserved Components Model [PDF]
, 2014 This paper employs an unobserved component model that incorporates a set of economic fundamentals to obtain the Euro-Dollar permanent equilibrium exchange rates (PEER) for the period 1975Q1 to 2008Q4.
MacDonald, Ronald, Chen, Xiaoshan
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