The purpose of this paper is to investigate the correlation between meteorological factors and Newcastle disease incidence, and to determine the key factors that affect Newcastle disease. Having built BP neural network forecasting model by Matlab 7.0 software, we tested the performance of the model according to the coefficient of determination (R2) and absolute values of the difference between ...

This paper examines a number of issues encountered when using standard health accounts data to forecast national health expenditures. In particular, it focuses on measurement issues, model specifications, and a comparison of performance indicators based on commonly used health accounts data from OECD. It assesses the performance of alternative forecasting methods based on three criteria — accur...

We evaluate various models’relative performance in forecasting future US output growth and in‡ation on a monthly basis. Our approach takes into account the possibility that the models’relative performance can be varying over time. We show that the models’ relative performance has, in fact, changed dramatically over time, both for revised and real-time data, and investigate possible factors that...

This study extends the existing forecasting accuracy debate in the tourism literature by examining the forecasting performance of various vector autoregressive (VAR) models. In particular, this study seeks to ascertain whether the introduction of the Bayesian restrictions (priors) to the unrestricted VAR process would lead to an improvement in forecasting performance in terms of achieving a hig...

A large literature in exchange rate economics has investigated the forecasting performance of empirical exchange rate models using conventional point forecast accuracy criteria. However, in the context of managing exchange rate risk, interest centers on more than just point forecasts. This paper provides a formal evaluation of recent exchange rate models based on the term structure of forward e...

The performance of financial forecasting with neural networks dependents on the particular training set. We design mean-change-point test to divide the original dataset into different training sets. The experiment results show that the larger training set does not necessarily produce better forecasting performance. Although the original datasets are different, the change-points to produce the o...

To improve the detection of the economic ”danger zones” from which severe banking crises emanate, this paper introduces classification tree ensembles to the banking crisis forecasting literature. I show that their out-of-sample performance in forecasting binary banking crisis indicators surpasses current best-practice early warning systems based on logit models by a substantial margin. I obtain...

Background and aims: Since accurate forecasts help inform decisions for preventive health-careintervention and epidemic control, this goal can only be achieved by making use of appropriatetechniques and methodologies. As much as forecast precision is important, methods and modelselection procedures are critical to forecast precision. This study aimed at providing an overview o...

This paper focuses on the study of short term load forecasting (STELF) using interval Type-2 Fuzzy Logic (IT2FL) and feed-forward Neural Network with back-propagation (NN-BP) tuning algorithm to improve their approximation capability, flexibility and adaptiveness. IT2FL for STELF is presented which provides additional degrees of freedom for handling more uncertainties for improving prediction a...

We examine the relation between the importance for firms to meet external performance benchmarks and the role of internal forecasting and misreporting for increasing the likelihood of meeting benchmarks. Drawing on survey data from investment centers, we hypothesize and find that the importance of meeting benchmarks is positively associated with the sophistication of firms’ internal forecasting...