Evaluation of Artificial Immune System with Artificial Neural Network for Predicting Bombay Stock Exchange Trends

2011 ◽  
Author(s):  
M. Gunasekaran ◽  
K.S Ramaswami
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Immune System ◽  
Artificial Immune System ◽  
Stock Exchange ◽  
Artificial Immune ◽  
Artificial Neural

Bike sharing demand prediction using artificial immune system and artificial neural network

2017 ◽  
Vol 23 (2) ◽  
pp. 613-626 ◽  
Author(s):  
Pei-Chann Chang ◽  
Jheng-Long Wu ◽  
Yahui Xu ◽  
Min Zhang ◽  
Xiao-Yong Lu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Immune System ◽  
Artificial Immune System ◽  
Artificial Immune ◽  
Demand Prediction ◽  
Bike Sharing ◽  
Artificial Neural

A Novel Intuitionistic Fuzzy Time Series Method Based on Bootstrapped Combined Pi-Sigma Artificial Neural Network

2021 ◽  
Vol 01 ◽  
Author(s):  
Eren Bas ◽  
Erol Egrioglu ◽  
Emine Kölemen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Stock Exchange ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Intuitionistic Fuzzy ◽  
Forecasting Methods ◽  
Time Series Method ◽  
Artificial Neural

Background: Intuitionistic fuzzy time series forecasting methods have been started to solve the forecasting problems in the literature. Intuitionistic fuzzy time series methods use both membership and non-membership values as auxiliary variables in their models. Because intuitionistic fuzzy sets take into consideration the hesitation margin and so the intuitionistic fuzzy time series models use more information than fuzzy time series models. The background of this study is about intuitionistic fuzzy time series forecasting methods. Objective: The study aims to propose a novel intuitionistic fuzzy time series method. It is expected that the proposed method will produce better forecasts than some selected benchmarks. Method: The proposed method uses bootstrapped combined Pi-Sigma artificial neural network and intuitionistic fuzzy c-means. The combined Pi-Sigma artificial neural network is proposed to model the intuitionistic fuzzy relations. Results and Conclusion: The proposed method is applied to different sets of SP&500 stock exchange time series. The proposed method can provide more accurate forecasts than established benchmarks for the SP&500 stock exchange time series. The most important contribution of the proposed method is that it creates statistical inference: probabilistic forecasting, confidence intervals and the empirical distribution of the forecasts. Moreover, the proposed method is better than the selected benchmarks for the SP&500 data set.

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