scholarly journals A Hybrid Forecasting Framework with Neural Network and Time-Series Method for Intermittent Demand in Semiconductor Supply Chain

2018 ◽  
pp. 65-72 ◽  
Author(s):  
Wenhan Fu ◽  
Chen-Fu Chien ◽  
Zih-Hao Lin
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Supply Chain ◽  
Series Method ◽  
Intermittent Demand ◽  
Hybrid Forecasting ◽  
Time Series Method

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.

Iris Recognition Based on Pulsed Coupled Neural Networks

2013 ◽  
Vol 380-384 ◽  
pp. 2637-2640
Author(s):  
Hai Ying Deng ◽  
Dong Ming Zhou ◽  
Ren Can Nie ◽  
Xiang Li ◽  
Hong Mei Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Iris Recognition ◽  
Series Method ◽  
Recognition Method ◽  
One Dimensional ◽  
Coupled Neural Networks ◽  
Time Series Method ◽  
The One

Based on Pulsed Coupled Neural Network Model, respectively to study the one-dimensional time series, row time series, column time series will keep some invariance by image migration. To apply this nature in iris recognition field, found that to extraction features using row time series method, which can resist rotation changes between the same samples, and keep enough details to distinguish between different samples. Compared with the traditional iris recognition method which using iris phase structure encoded, the results show that row time series method, is less sensitive for phase, and can resist rotation changes better, is effective.

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.

Waveform computations by the time-series method

1952 ◽  
Vol 1952 (10) ◽  
pp. 246-246 ◽  
Author(s):  
N.W. Lewis
Keyword(s):  
Time Series ◽  
Series Method ◽  
Time Series Method

scholarly journals IMPLEMENTASI METODE FUZZY TIME SERIES DENGAN MODEL ALGORITMA CHEN UNTUK MEMPREDIKSI HARGA EMAS

2020 ◽  
Vol 13 (1) ◽  
pp. 71-78
Author(s):  
Darsono Nababan ◽  
Eric Alexander
Keyword(s):  
Time Series ◽  
Interest Rates ◽  
Gold Price ◽  
Fuzzy Time Series ◽  
Actual Data ◽  
Series Method ◽  
Market Uncertainty ◽  
Low Interest Rates ◽  
Calculation Results ◽  
Time Series Method

Gold is one of the people's preferred forms of investment and is considered the safest (save -heaven). Gold risk which is considered small is the main attraction because in general Indonesian people are not yet familiar with capital market investments such as stocks and mutual funds. But the price of gold is very volatile as for the factors that affect the fluctuations of gold are consumption demand, volatility and market uncertainty, protection of low-interest rates, and the US dollar. Predicting the movement of the gold price and knowing where the direction of the exchange rate moves and determining the price of gold up or down cannot be done accurately and consistently. For this reason, in reducing the risk of loss, an application is needed to predict gold prices using the Fuzzy Time Series Chen algorithm using MATLAB software. In this study to obtain prediction results and comparison charts using actual data and prediction data for the 2015-2017 gold price. From the calculation results obtained by the prediction results with the Fuzzy Time Series method with the Chen algorithm where the average difference between the actual data and prediction data is not more than Rp. 2,850, - where predictions using the Fuzzy Time Series method Chen's algorithm is sufficient to use 1 data to predict the second data which makes this method accurate in predicting the price of gold.

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