Computational Intelligence Methods for Financial Forecasting
نویسندگان
چکیده
Forecasting the short run behavior of foreign exchange rates is a challenging problem that has attracted considerable attention. High frequency financial data are typically characterized by noise and non–stationarity. In this work we investigate the profitability of a forecasting methodology based on unsupervised clustering and feedforward neural networks and compare its performance with that of a single feedforward neural network and nearest neighbor regression. The experimental results indicate that the proposed combination of the two methodologies achieves a higher profit.
منابع مشابه
A hybrid computational intelligence model for foreign exchange rate forecasting
Computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. Forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. Nowadays, despite the numerous time series forecasting models propos...
متن کاملA hybrid computational intelligence model for foreign exchange rate forecasting
Abstract: Computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. Forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. Nowadays, despite the numerous time series forecasting mod...
متن کاملForecasting of Financial Markets via Neural Network
Artificial neural network is one of the intelligent methods in Artificial Intelligence. There are many decisions of different tasks using neural network approach. The forecasting problems are high challenge and researchers use different methods to solve them. The financial tasks related to forecasting, classification and management using artificial neural network are considered. The technology ...
متن کاملA hybrid approach for modeling financial time series
The problem we tackle concerns forecasting time series in financial markets. AutoRegressive Moving-Average (ARMA) methods and computational intelligence have also been used to tackle this problem. We propose a novel method for time series forecasting based on a hybrid combination of ARMA and Gene Expression Programming (GEP) induced models. Time series from financial domains often encapsulate d...
متن کاملElectricity Load Forecasting by Combining Adaptive Neuro-fuzzy Inference System and Seasonal Auto-Regressive Integrated Moving Average
Nowadays, electricity load forecasting, as one of the most important areas, plays a crucial role in the economic process. What separates electricity from other commodities is the impossibility of storing it on a large scale and cost-effective construction of new power generation and distribution plants. Also, the existence of seasonality, nonlinear complexity, and ambiguity pattern in electrici...
متن کامل