scholarly journals Fuzzy Decision Tree and Particle Swarm Optimization for Mining of Time Series Data

2011 ◽  
Vol 17 (7) ◽  
pp. 35-41
Author(s):  
Maya Nayak ◽  
Satyabrata Dash
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Decision Tree ◽  
Time Series Data ◽  
Particle Swarm ◽  
Series Data ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Swarm Optimization

Prediction of Financial Time Series Data using Hybrid Evolutionary Legendre Neural Network

2016 ◽  
Vol 7 (1) ◽  
pp. 16-32 ◽  
Author(s):  
Rajashree Dash ◽  
Pradipta Kishore Dash
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Particle Swarm Optimization ◽  
Time Series Data ◽  
Financial Time Series ◽  
Random Search ◽  
Particle Swarm ◽  
Series Data ◽  
Swarm Optimization ◽  
Financial Time

In this paper a predictor model using Legendre Neural Network is proposed for one day ahead prediction of financial time series data. The Legendre Neural Network (LENN) is a single layer structure that possess faster convergence rate and reduced computational complexity by increasing the dimensionality of the input pattern with a set of linearly independent nonlinear functions. The parameters of the LENN model are estimated using a Moderate Random Search Particle Swarm Optimization Method (HMRPSO). The HMRPSO is a variant of PSO that uses a moderate random search method to enhance the global search ability of particles and increases their convergence rates by focusing on valuable search space regions. Training LENN using HMRPSO has also been compared with Particle Swarm Optimization (PSO) and Differential Evolution (DE) based learning of LENN for predicting the Bombay Stock Exchange and S&P 500 data sets.

Evolving and clustering fuzzy decision tree for financial time series data forecasting

2009 ◽  
Vol 36 (2) ◽  
pp. 3761-3773 ◽  
Author(s):  
Robert K. Lai ◽  
Chin-Yuan Fan ◽  
Wei-Hsiu Huang ◽  
Pei-Chann Chang
Keyword(s):  
Time Series ◽  
Decision Tree ◽  
Time Series Data ◽  
Financial Time Series ◽  
Series Data ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Financial Time

Developing intuitionistic fuzzy seasonality regression with particle swarm optimization for air pollution forecasting

2019 ◽  
Vol 119 (3) ◽  
pp. 561-577 ◽  
Author(s):  
Chung-Han Ho ◽  
Ping-Teng Chang ◽  
Kuo-Chen Hung ◽  
Kuo-Ping Lin
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Time Series Data ◽  
Series Data ◽  
Swarm Optimization ◽  
Content Type ◽  
Intuitionistic Fuzzy ◽  
Seasonal Time Series ◽  
Air Pollutions ◽  
Daily Concentration

PurposeThe purpose of this paper is to develop a novel intuitionistic fuzzy seasonality regression (IFSR) with particle swarm optimization (PSO) algorithms to accurately forecast air pollutions, which are typical seasonal time series data. Seasonal time series prediction is a critical topic, and some time series data contain uncertain or unpredictable factors. To handle such seasonal factors and uncertain forecasting seasonal time series data, the proposed IFSR with the PSO method effectively extends the intuitionistic fuzzy linear regression (IFLR).Design/methodology/approachThe prediction model sets up IFLR with spreads unrestricted so as to correctly approach the trend of seasonal time series data when the decomposition method is used. PSO algorithms were simultaneously employed to select the parameters of the IFSR model. In this study, IFSR with the PSO method was first compared with fuzzy seasonality regression, providing evidence that the concept of the intuitionistic fuzzy set can improve performance in forecasting the daily concentration of carbon monoxide (CO). Furthermore, the risk management system also implemented is based on the forecasting results for decision-maker.FindingsSeasonal autoregressive integrated moving average and deep belief network were then employed as comparative models for forecasting the daily concentration of CO. The empirical results of the proposed IFSR with PSO model revealed improved performance regarding forecasting accuracy, compared with the other methods.Originality/valueThis study presents IFSR with PSO to accurately forecast air pollutions. The proposed IFSR with PSO model can efficiently provide credible values of prediction for seasonal time series data in uncertain environments.

Sign in / Sign up

Export Citation Format

Share Document