Name
Title | ||
|---|---|---|
An increasing hybrid morphological-linear perceptron with evolutionary learning and phase correction for financial time series forecasting |
Abstract | ||
|---|---|---|
In this paper we present a suitable model to solve the financial time series forecasting problem, called increasing hybrid morphological-linear perceptron (IHMP) An evolutionary training algorithm is presented to design the IHMP (learning process), using a modified genetic algorithm (MGA) The learning process includes an automatic phase correction step that is geared at eliminating the time phase distortions that typically occur in financial time series forecasting Furthermore, we compare the proposed IHMP with other neural and statistical models using two complex nonlinear problems of financial forecasting. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-13803-4_44 | HAIS (2) |
Keywords | Field | DocType |
hybrid morphological-linear perceptron,financial time series forecasting,evolutionary training algorithm,automatic phase correction step,time phase distortion,evolutionary learning,modified genetic algorithm,statistical model,complex nonlinear problem,proposed ihmp,financial forecasting,neural network,genetic algorithm,lattice theory | Financial forecasting,Nonlinear system,Computer science,Financial time series forecasting,Statistical model,Artificial intelligence,Evolutionary learning,Perceptron,Phase correction,Machine learning,Genetic algorithm | Conference |
Volume | ISSN | ISBN |
6077 | 0302-9743 | 3-642-13802-0 |
Citations | PageRank | References |
2 | 0.37 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ricardo De A. Araújo | 1 | 248 | 19.46 |
| Peter Sussner | 2 | 880 | 59.25 |