Computational Intelligence in Economics and Finance: Volume II, 2. sējumsPaul P. Wang, Tzu-Wen Kuo Springer Science & Business Media, 2007. gada 11. jūl. - 228 lappuses Computational intelligence (CI), as an alternative to statistical and econometric approaches, has been applied to a wide range of economics and finance problems in recent years, for example to price forecasting and market efficiency. This book contains research ranging from applications in financial markets and business administration to various economics problems. Not only are empirical studies utilizing various CI algorithms presented, but so also are theoretical models based on computational methods. In addition to direct applications of computational intelligence, readers can also observe how these methods are combined with conventional analytical methods such as statistical and econometric models to yield preferred results. Chen, Wang, and Kuo have grouped the 12 contributions following their introductory chapter into applications of fuzzy logic, neural networks (including self-organizing maps and support vector machines), and evolutionary computation. All chapters were selected either by invitation or based on a careful selection and extension of best papers from the International Workshop on Computational Intelligence in Economics and Finance in 2005. Overall, the book offers researchers an excellent overview of current advances and applications of computational intelligence techniques to economics and finance problems. |
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1.–5. rezultāts no 34.
... average series, MA(q), effectively. To see this, consider an MA(1) series as follows. xt = εt − θ1εt−1. (1) It is well-known that if | θ1 |< 1, then the above MA(1) series can also be written as an AR(∞) series. xt = − ∞∑ i=1 θixt ...
... average (ARMA) models xt = f(xt−1, ..., xt−p ,ε t−1, ...εt−q) + εt = f(xt−1, ..., xt−p ,x t−p−1, ...) + εt (4) The finite memory problem of the multilayer perceptron neural net is well noticed by ANN researchers. In his ...
... average in the forecast, s∈N(Xmt) xs+1 k , (33) ˆxt+1 = ∑ 15 The notation ˆf is used, instead of f, to reserve f for the true relation, if it exists, and in that case, ˆf is the estimation of f. In addition, there are variations when ...
... average based on the distance of each member. The same idea can be applied to deal with the linear conditional mean (linear re- gression model): we can either take the ordinal least squares or the weighted least squares.17 From the ...
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Saturs
1 | |
An Overview of Insurance Uses of Fuzzy Logic | 24 |
ArnoldF Shapiro 25 | 63 |
Estimating Female Labor Force Participation through Statistical | 93 |
An Application of Kohonens SOFM to the Management | 106 |
Trading Strategies Based on Kmeans Clustering and Regression Models | 123 |
Application of an Instance Based Learning Algorithm for Predicting | 144 |
Nonlinear GoalDirected CPPI Strategy | 183 |
A LogicalHeuristic Approach | 209 |
Index | 224 |
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Computational Intelligence in Economics and Finance: Volume II Paul P. Wang,Tzu-Wen Kuo Priekšskatījums nav pieejams - 2010 |
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