Next day price forecasting in deregulated market by combination of Artificial Neural Network and ARIMA time series models

2010 ◽  
Author(s):  
Phatchakorn Areekul ◽  
Tomonobu Senjyu ◽  
Naomitsu Urasaki ◽  
Atsushi Yona
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Time Series Models ◽  
Price Forecasting ◽  
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.

A comparison of artificial neural network and time series models for forecasting commodity prices

1996 ◽  
Vol 10 (2) ◽  
pp. 169-181 ◽  
Author(s):  
Nowrouz Kohzadi ◽  
Milton S. Boyd ◽  
Bahman Kermanshahi ◽  
Iebeling Kaastra
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Commodity Prices ◽  
Time Series Models ◽  
Artificial Neural
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