๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Forecasting tourism demand based on empirical mode decomposition and neural network

โœ Scribed by Chun-Fu Chen; Ming-Cheng Lai; Ching-Chiang Yeh


Book ID
113771819
Publisher
Elsevier Science
Year
2012
Tongue
English
Weight
553 KB
Volume
26
Category
Article
ISSN
0950-7051

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


Back-propagation learning in improving t
โœ Rob Law ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 184 KB

Traditional tourism demand forecasting techniques concentrate predominantly on multivariate regression models and univariate time-series models. These single mathematical function-based forecasting techniques, although they have achieved a certain degree of success in tourism forecasting, are unable

Prediction of Chemical Oxygen Demand (CO
โœ Davut Hanbay; Ibrahim Turkoglu; Yakup Demir ๐Ÿ“‚ Article ๐Ÿ“… 2007 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 733 KB

## Abstract The chemical oxygen demand (COD) parameter of a wastewater treatment plant is predicted based on wavelet decomposition, entropy, and neural networks (NN) for rapid COD analysis. This paper also describes the usage of wavelet and NNs for parameter prediction. Data from a wastewater treat