Hybrid Swarm System for Time Series Forecasting
نویسندگان
چکیده
In this paper, a hybrid swarm system is presented for time series forecasting. It consists of an intelligent hybrid model composed of an Arti cial Neural Network (ANN) and a Particle Swarm Optimizer (PSO), which search the relevant time lags for a correct characterization of the time series, as well as the number of processing units in the hidden layer, the training algorithm and the modeling of ANN. The proposed method shows to be an ef cient procedure to training and adjusting the ANN parameters through the use of a particle swarm optimization mechanism. An experimental analysis is conducted with the proposed method using four real world time series and the results are compared to standard MLP networks according to ve performance measures.
منابع مشابه
Time Variant Fuzzy Time Series Approach for Forecasting Using Particle Swarm Optimization
Fuzzy time series have been developed during the last decade to improve the forecast accuracy. Many algorithms have been applied in this approach of forecasting such as high order time invariant fuzzy time series. In this paper, we present a hybrid algorithm to deal with the forecasting problem based on time variant fuzzy time series and particle swarm optimization algorithm, as a highly effi...
متن کاملComputational Intelligence Hybrid Learning Approach to Time Series Forecasting
Time series forecasting is an important and widely popular topic in the research of system modeling. This paper describes how to use the hybrid PSO-RLSE neuro-fuzzy learning approach to the problem of time series forecasting. The PSO algorithm is used to update the premise parameters of the proposed prediction system, and the RLSE is used to update the consequence parameters. Thanks to the hybr...
متن کاملIntelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence
Financial investors often face an urgent need to predict the future. Accurate forecasting may allow investors to be aware of changes in financial markets in the future, so that they can reduce the risk of investment. In this paper, we present an intelligent computing paradigm, called the Complex Neuro-Fuzzy System (CNFS), applied to the problem of financial time series forecasting. The CNFS is ...
متن کاملAN EXTENDED FUZZY ARTIFICIAL NEURAL NETWORKS MODEL FOR TIME SERIES FORECASTING
Improving time series forecastingaccuracy is an important yet often difficult task.Both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. In this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...
متن کاملWhich Methodology is Better for Combining Linear and Nonlinear Models for Time Series Forecasting?
Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model. It is especially occurred when the models in the ensemble are quite different. Hybrid techniques that decompose a time series into its linear and nonlinear components are one of the most important kinds of the hybrid model...
متن کاملA Particle Swarm Optimization Algorithm for Forecasting Based on Time Variant fuzzy Time Series
Fuzzy time series have been developed during the last decade to improve the forecast accuracy. Many algorithms have been applied in this approach of forecasting such as high order time invariant fuzzy time series. In this paper, we present a hybrid algorithm to deal with the forecasting problem based on time variant fuzzy time series and particle swarm optimization algorithm, as a highly effici...
متن کامل