scholarly journals The Efficiency of Bankruptcy Forecast Models in the Hungarian SME Sector

2014 ◽  
Vol 6 (2) ◽  
pp. 56-73 ◽  
Author(s):  
Kristof Szeverin Ekes ◽  
◽  
Laszlo Koloszar
Keyword(s):  
Forecast Models

Data mining using example-based methods in oceanographic forecast models

1998 ◽  
Author(s):  
J.M. Corchado ◽  
N. Rees ◽  
B. Lees ◽  
J. Aiken
Keyword(s):  
Data Mining ◽  
Forecast Models

scholarly journals A warm jet in a cold ocean

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jennifer A. MacKinnon ◽  
Harper L. Simmons ◽  
John Hargrove ◽  
Jim Thomson ◽  
Thomas Peacock ◽  
...  
Keyword(s):  
The Arctic ◽  
Bering Strait ◽  
Near Surface ◽  
Ice Melt ◽  
Beaufort Gyre ◽  
The Pacific ◽  
Climate Simulations ◽  
Initial Evolution ◽  
Forecast Models

AbstractUnprecedented quantities of heat are entering the Pacific sector of the Arctic Ocean through Bering Strait, particularly during summer months. Though some heat is lost to the atmosphere during autumn cooling, a significant fraction of the incoming warm, salty water subducts (dives beneath) below a cooler fresher layer of near-surface water, subsequently extending hundreds of kilometers into the Beaufort Gyre. Upward turbulent mixing of these sub-surface pockets of heat is likely accelerating sea ice melt in the region. This Pacific-origin water brings both heat and unique biogeochemical properties, contributing to a changing Arctic ecosystem. However, our ability to understand or forecast the role of this incoming water mass has been hampered by lack of understanding of the physical processes controlling subduction and evolution of this this warm water. Crucially, the processes seen here occur at small horizontal scales not resolved by regional forecast models or climate simulations; new parameterizations must be developed that accurately represent the physics. Here we present novel high resolution observations showing the detailed process of subduction and initial evolution of warm Pacific-origin water in the southern Beaufort Gyre.

scholarly journals A systematic approach to construct credit risk forecast models

2011 ◽  
Vol 31 (1) ◽  
pp. 41-56 ◽  
Author(s):  
Lisiane Priscila Roldão Selau ◽  
José Luis Duarte Ribeiro
Keyword(s):  
Credit Risk ◽  
Systematic Approach ◽  
Forecast Models ◽  
Risk Forecast

Forecasting the impacts of climate variability: lessons from the rainfed corn market in Ceará, Brazil

2008 ◽  
Vol 13 (2) ◽  
pp. 201-227 ◽  
Author(s):  
ARIASTER B. CHIMELI ◽  
FRANCISCO DE ASSIS DE SOUZA FILHO ◽  
MARCOS COSTA HOLANDA ◽  
FRANCIS CARLO PETTERINI
Keyword(s):  
Climate Variability ◽  
Developing Economies ◽  
Arid Zone ◽  
Well Being ◽  
Policy Makers ◽  
Local Climate ◽  
The World ◽  
Forecast Models ◽  
Climatic Impacts ◽  
Semi Arid

ABSTRACTA number of studies show that climatic shocks have significant economic impacts in several regions of the world, especially in, but not limited to, developing economies. In this paper we focus on a drought-related indicator of well-being and emergency spending in the Brazilian semi-arid zone – rainfed corn market – and estimate aggregate behavioral and forecast models for this market conditional on local climate determinants. We find encouraging evidence that our approach can help policy makers buy time to help them prepare for drought mitigating actions. The analysis is applicable to economies elsewhere in the world and climatic impacts other than those caused by droughts.

Modeling CO2 Emissions of Top 5 Emitters with Combination Technique

2012 ◽  
Vol 616-618 ◽  
pp. 1143-1147
Author(s):  
Wei Sun ◽  
Jing Min Wang ◽  
Jun Jie Kang
Keyword(s):  
Forecast Error ◽  
Combination Method ◽  
Future Projection ◽  
Forecasting Accuracy ◽  
Combination Technique ◽  
Combination Forecast ◽  
Forecast Models ◽  
Model Time Series ◽  
Grey Verhulst Model ◽  
Better Than

In this paper, the performance of combination forecast methods for CO2 emissions prediction is investigated. Linear model, time series model, GM (1, 1) model and Grey Verhulst model are selected in study as the separate models. And, four kinds of combination forecast models, i.e. the equivalent weight (EW) combination method, variance-covariance (VACO) combination method, regression combination (R) method, and discounted mean square forecast error (MSFE) method are chosen to employ for top 5 CO2 emitters. The forecasting accuracy is compared between these combination models and single models. This research suggests that the combination forecasts are almost certain to outperform the worst individual forecasts and maybe even better than most individual ones. Furthermore the combination forecasts can avoid the risk of model choosing in future projection. For CO2 emissions forecast with many uncertain factors in the future, combining the single forecast would be safer in such forecasting situations.

scholarly journals Forecast models for actual construction time and cost

2003 ◽  
Vol 38 (8) ◽  
pp. 1075-1083 ◽  
Author(s):  
R Martin Skitmore ◽  
S Thomas Ng
Keyword(s):  
Construction Time ◽  
Forecast Models
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