System identification: theory for the user
System identification: theory for the user
Forecasting S&P 500 stock index futures with a hybrid AI system
Decision Support Systems
An ARMA order selection method with fuzzy reasoning
Signal Processing - Special section on information theoretic aspects of digital watermarking
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Building ARMA Models with Genetic Algorithms
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
A hybrid genetic-neural architecture for stock indexes forecasting
Information Sciences: an International Journal - Special issue: Computational intelligence in economics and finance
Comparison among five evolutionary-based optimization algorithms
Advanced Engineering Informatics
Expert Systems with Applications: An International Journal
Tax forecasting theory and model based on SVM optimized by PSO
Expert Systems with Applications: An International Journal
A hybrid linear-neural model for time series forecasting
IEEE Transactions on Neural Networks
Hi-index | 12.05 |
A time series forecasting is an active research applied significantly in a variety of economics areas. Over the past three decades an auto-regressive integrated moving average (ARIMA) model, as one of the most important time series models, has been applied in financial markets forecasting. Recent researches in time series forecasting ARIMA models indicate some basic limitations which detract from their popularities for financial time series forecasting: One limitation of an ARIMA model is that it requires a large amount of historical data to generate an accurate result. Both theoretical and empirical findings suggest that combining different time series models may be an effective method of improving the predictive performances of data especially when the models in the ensemble are quite different. The main purpose of present paper is to combine the ARIMA model with the particle swarm optimization (PSO) model in order to improve and generate more accurate forecasting results. Under small data information, combining the PSO and ARIMA models performs better performance results compared to an ARIMA model itself. The proposed model is robust and it may be used as an alternative forecasting tool in economics areas.