The Scientific and Policy Needs for Improved Health Forecasting Models for Elderly Populations

In the article the author examines the problems of wide application of the bankruptcy formula. For this, many terms have been considered, in particular the definition of financial insolvency, which characterizes the weaknesses of enterprises, namely, those associated with loss of liquidity and operating losses. The results show that Altman's bankruptcy formula is easily applicable in the economic conditions of the Russian Federation and is useful for predicting financial difficulties given the established definition of financial insolvency. Due to the fact that this term combines the factors of liquidity, stability, an important component of the Altman Z-model is the factor of independence, which is aimed at solving the problems that organizations face.

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Apport des campagnes de mesures pour la compréhension des interactions sol-végétation-atmosphère

La Météorologie ◽

10.37053/lameteorologie-2020-0015 ◽

2020 ◽

pp. 046

Author(s):

Thierry Bergot ◽

Pierre Bessemoulin ◽

Claire Sarrat

Keyword(s):

Numerical Modeling ◽

Field Experiments ◽

Surface Processes ◽

Surface Model ◽

Modélisation Numérique ◽

Forecasting Models ◽

Operational Forecasting ◽

Bilan Hydrique

La synergie entre campagnes de mesures et modélisation numérique a permis de faire fortement progresser notre compréhension des interactions sol-végétation-atmosphère. Ces progrès ont conduit à l'élaboration du modèle de surface Isba développé par Joël Noilhan et utilisé aujourd'hui encore dans les modèles de prévision opérationnelle de Météo-France. Cet article vise à illustrer l'apport des campagnes de mesures dans l'amélioration de nos connaissances des processus en surface à travers trois exemples, Hapex-Mobilhy pour l'étude du bilan hydrique, Carbo-Europe pour l'étude du bilan en carbone et l'étude du brouillard sur l'aéroport Paris-CdG pour l'influence des conditions de surface sur les nuages bas. The synergy between field experiments and numerical modeling has allowed to significantly advance our understanding of soil-vegetation-atmosphere interactions. This progress led to the ISBA surface model developed by Joël Noilhan and used today in Météo-France's operational forecasting models. This article aims to illustrate the contribution of field experiments in improving our knowledge of surface processes through three examples: Hapex-Mobilhy, Carbo-Europe, and the study of fog at Paris-CdG airport.

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Temporal dependence in multi-step density forecasting models

The Journal of Risk Model Validation ◽

10.21314/jrmv.2007.001 ◽

2007 ◽

Vol 1 (1) ◽

pp. 3-22

Author(s):

Kevin Dowd

Keyword(s):

Temporal Dependence ◽

Forecasting Models ◽

Density Forecasting ◽

Step Density

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Improving Disease Outbreak Forecasting Models for Efficient Targeting of Public Health Resources

SSRN Electronic Journal ◽

10.2139/ssrn.3072086 ◽

2017 ◽

Author(s):

Lasantha Fernando ◽

Sriganesh Lokanathan ◽

Amal Shehan Perera ◽

Azhar Ghouse ◽

Hasitha Tissera

Keyword(s):

Public Health ◽

Health Resources ◽

Disease Outbreak ◽

Forecasting Models

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A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation

Entropy ◽

10.3390/e21050455 ◽

2019 ◽

Vol 21 (5) ◽

pp. 455 ◽

Cited By ~ 5

Author(s):

Hongjun Guan ◽

Zongli Dai ◽

Shuang Guan ◽

Aiwu Zhao

Keyword(s):

Time Series ◽

Information Entropy ◽

Stock Exchange ◽

Time Series Forecasting ◽

High Order ◽

Stock Index ◽

Neutrosophic Set ◽

Forecasting Model ◽

Forecasting Models ◽

Proposed Model

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

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