A new fuzzy time series method based on an ARMA-type recurrent Pi-Sigma artificial neural network

2019 ◽  
Vol 24 (11) ◽  
pp. 8243-8252 ◽  
Author(s):  
Cem Kocak ◽  
Ali Zafer Dalar ◽  
Ozge Cagcag Yolcu ◽  
Eren Bas ◽  
Erol Egrioglu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Fuzzy Time Series ◽  
Series Method ◽  
Time Series Method ◽  
Artificial Neural

scholarly journals Fuzzy Time Series and Artificial Neural Network: Forecasting Exportation of Natural Rubber in Malaysia

2021 ◽  
Vol 6 (1) ◽  
pp. 22-30
Author(s):  
Siti Nor Nadrah Muhamad ◽  
Shafeina Hatieqa Sofean ◽  
Balkiah Moktar ◽  
Wan Nurshazelin Wan Shahidan
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Natural Rubber ◽  
Mean Square Error ◽  
Fuzzy Time Series ◽  
Mean Square ◽  
Series Method ◽  
Time Series Method ◽  
Artificial Neural

Natural rubber is one of the most important crops in Malaysia alongside palm oil, cocoa, paddy, and pineapple. Being a tropical country, Malaysia is one of the top five exporters and producers of rubber in the world. The purpose of this study is to find the forecasted value of the actual data of the number of exportations of natural rubber by using Fuzzy Time Series and Artificial Neural Network. This study is also conducted to determine the best model by making comparison between Fuzzy Time Series and Artificial Neural Network. Fuzzy Time Series has allowed to overcome a downside where the classical time series method cannot deal with forecasting problem in which values of time series are linguistic terms represented by fuzzy sets. Artificial Neural Network was introduced as one of the systematic tools of modelling which has been forecasting for about 20 years ago. The error measure that was used in this study to make comparisons were Mean Square Error, Root Mean Square Error and Mean Absolute Percentage Error. The results of this study showed that the fuzzy time series method has the smallest error value compared to artificial neural network which means it was more accurate compared to artificial neural network in forecasting exportation of natural rubber in Malaysia.

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.

Forecasting of Air Pollution Index with Artificial Neural Network

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
Nur Haizum Abd Rahman ◽  
Muhammad Hisyam Lee ◽  
Mohd Talib Latif ◽  
Suhartono S.
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Time Series ◽  
Artificial Neural Network ◽  
Air Quality ◽  
Warning System ◽  
Pollution Index ◽  
Fuzzy Time Series ◽  
Air Pollution Index ◽  
Artificial Neural

In recent years, the arisen of air pollution in urban area address much attention globally. The air pollutants has emerged detrimental effects on health and living conditions. Time series forecasting is the important method nowadays with the ability to predict the future events. In this study, the forecasting is based on 10 years monthly data of Air Pollution Index (API) located in industrial and residential monitoring stations area in Malaysia. The autoregressive integrated moving average (ARIMA), fuzzy time series (FTS) and artificial neural network (ANNs) were used as the methods to forecast the API values. The performance of each method is compare using the root mean square error (RMSE). The result shows that the ANNs give the smallest forecasting error to forecast API compared to FTS and ARIMA. Therefore, the ANNs could be consider a reliable approach in early warning system to general public in understanding the air quality status that might effect their health and also in decision making processes for air quality control and management.

scholarly journals Trajectory of COVID-19 Data in India: Investigation and Project Using Artificial Neural Network, Fuzzy Time Series and ARIMA Models

2020 ◽  
pp. 46-54
Author(s):  
Pradeep Mishra ◽  
Chellai Fatih ◽  
Deepa Rawat ◽  
Saswati Sahu ◽  
Sagar Anand Pandey ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Arima Model ◽  
Fuzzy Time Series ◽  
Short Term ◽  
Indian Health ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Impact

Due to the impact of Corona virus (COVID-19) pandemic that exists today, all countries, national and international organizations are in a continuous effort to find efficient and accurate statistical models for forecasting the future pattern of COVID infection. Accurate forecasting should help governments to take decisive decisions to master the pandemic spread.  In this article, we explored the COVID-19 database of India between 17th March to 1st July 2020, then we estimated two nonlinear time series models: Artificial Neural Network (ANN) and Fuzzy Time Series (FTS) by comparing them with ARIMA model. In terms of model adequacy, the FTS model out performs the ANN for the new cases and new deaths time series in India. We observed a short-term virus spread trend according to three forecasting models.Such findings help in more efficient preparation for the Indian health system.

scholarly journals A Hybrid Fuzzy Time Series Approach Based on Fuzzy Clustering and Artificial Neural Network with Single Multiplicative Neuron Model

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Ozge Cagcag Yolcu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Fuzzy Clustering ◽  
Neuron Model ◽  
Fuzzy Relation ◽  
Optimization Techniques ◽  
Fuzzy Time Series ◽  
Target Values ◽  
Artificial Neural

Particularly in recent years, artificial intelligence optimization techniques have been used to make fuzzy time series approaches more systematic and improve forecasting performance. Besides, some fuzzy clustering methods and artificial neural networks with different structures are used in the fuzzification of observations and determination of fuzzy relationships, respectively. In approaches considering the membership values, the membership values are determined subjectively or fuzzy outputs of the system are obtained by considering that there is a relation between membership values in identification of relation. This necessitates defuzzification step and increases the model error. In this study, membership values were obtained more systematically by using Gustafson-Kessel fuzzy clustering technique. The use of artificial neural network with single multiplicative neuron model in identification of fuzzy relation eliminated the architecture selection problem as well as the necessity for defuzzification step by constituting target values from real observations of time series. The training of artificial neural network with single multiplicative neuron model which is used for identification of fuzzy relation step is carried out with particle swarm optimization. The proposed method is implemented using various time series and the results are compared with those of previous studies to demonstrate the performance of the proposed method.

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