three combination methods commonly used in tourism forecasting are the simple average method, the variance-covariance method and the discounted msfe method. these methods assign the different weights that can not change at each time point to each individual forecasting model. in this study, we introduce the iowga operator combination method which can overcome the defect of previous three combin...

An organization needs to develop a forecasting system involving several approaches to predicting uncertain events. Such forecasting systems require the development of expertise in identifying forecasting problems, applying a range of forecasting methods, selecting appropriate methods for each problem, and evaluating and refining forecasting methods over time. It is also important to have strong...

BACKGROUND Real-time forecasting of epidemics, especially those based on a likelihood-based approach, is understudied. This study aimed to develop a simple method that can be used for the real-time epidemic forecasting. METHODS A discrete time stochastic model, accounting for demographic stochasticity and conditional measurement, was developed and applied as a case study to the weekly inciden...

In this paper, we presented the performance of forecasting model and error correction will affect the accuracy of short-term load forecasting. Least squares support vector machines (LS-SVM) based on improved particle swarm optimization is selected as load forecasting model. Forecasting accuracy and generalization performance of LS-SVM depend on selection of its parameters greatly. Adaptive part...

Hybrid model is a popular forecasting model in renewable energy related forecasting applications. Wind speed forecasting, as a common application, requires fast and accurate forecasting models. This paper introduces an Empirical Mode Decomposition (EMD) followed by a k Nearest Neighbor (kNN) hybrid model for wind speed forecasting. Two configurations of EMD-kNN are discussed in details: an EMD-...

With increasing importance being attached to big data mining, analysis, and forecasting in the field of wind energy, how to select an optimization model to improve the forecasting accuracy of the wind speed time series is not only an extremely challenging problem, but also a problem of concern for economic forecasting. The artificial intelligence model is widely used in forecasting and data pro...

Artificial neural networks have emerged as an important quantitative modeling tool for business forecasting. This chapter provides an overview of forecasting with neural networks. We provide a brief description of neural networks, their advantages over traditional forecasting models, and their applications for business forecasting. In addition, we address several important modeling issues for f...

In order to review the present status of bioclimate research and weather-health forecasting in Japan, development is first described briefly for the periods of the first and second halves of the 20th Century. Important topics for further development are demonstrated to be: temperature change; lifestyle changes; historical approaches used such as weather-related proverbs, old documents, etc.; an...