Search Results - subject:"feedforward neural network"

Machine learning approaches to predict default of credit card clients

Liu, Ruilin - In: Modern economy 9 (2018) 11, pp. 1828-1838

Persistent link: https://www.econbiz.de/10011997659

Combining robust dynamic neural networks with traditional technical indicators for generating mechanic trading signals

Giribone, Pier Giuseppe; Ligato, Simone; Penone, Francesco - In: International journal of financial engineering 5 (2018) 4, pp. 1-44

Persistent link: https://www.econbiz.de/10012028821

Neural network application to support regression model in forecasting single-sectional demand for telecommunications services

Kaczmarczyk, Paweł - In: Folia oeconomica Stetinensia : FOS 18 (2018) 2, pp. 159-177

Persistent link: https://www.econbiz.de/10012159180

Ensemble forecasting for complex time series using sparse representation and neural networks

Yu, Lean; Zhao, Yang; Tang, Ling - In: Journal of forecasting 36 (2017) 2, pp. 122-138

Persistent link: https://www.econbiz.de/10011729109

The stability of electricity prices: estimation and inference of the Lyapunov exponents

Bask, Mikael; Liu, Tung; Widerberg, Anna - 2006

exponents. Specifically, we use a feedforward neural network to estimate these exponents as well as asymptotic results for this …

Persistent link: https://www.econbiz.de/10012147965

The stability of electricity prices: estimation and inference of the Lyapunov exponents

Bask, Mikael; Liu, Tung; Widerberg, Anna - Suomen Pankki - 2006

exponents. Specifically, we use a feedforward neural network to estimate these exponents as well as asymptotic results for this …

Persistent link: https://www.econbiz.de/10005190763

Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting

An, Ning; Zhao, Weigang; Wang, Jianzhou; Shang, Duo; … - In: Energy 49 (2013) C, pp. 279-288

-output FFNN (feedforward neural network) with EMD (empirical mode decomposition)-based signal filtering and seasonal adjustment …

Persistent link: https://www.econbiz.de/10010808872

An improved training algorithm for feedforward neural network learning based on terminal attractors

Yu, Xinghuo; Wang, Bin; Batbayar, Batsukh; Wang, Liuping; … - In: Journal of Global Optimization 51 (2011) 2, pp. 271-284

Persistent link: https://www.econbiz.de/10009324658

The stability of electricity prices: Estimation and inference of the Lyapunov exponents

Bask, Mikael; Liu, Tung; Widerberg, Anna - In: Physica A: Statistical Mechanics and its Applications 376 (2007) C, pp. 565-572

exponents. Specifically, we use a feedforward neural network to estimate these exponents as well as asymptotic results for this …

Persistent link: https://www.econbiz.de/10010588713

Predicting Stock Returns

Panda, Chakradhara; Narasimhan, V. - In: South Asia Economic Journal 7 (2006) 2, pp. 205-218

In this article, we use the artificial neural network in the forecasting of daily Bombay Stock Exchange (BSE) Sensitive Index (Sensex) returns. We compare the performance of the neural network with performances of random walk and linear autoregressive models by using six performance measures....

Persistent link: https://www.econbiz.de/10010784376

A service of the

zbw