Stock Market Forecasting Model Based on AR(1) with Adjusted Triangular Fuzzy Number Using Standard Deviation Approach for ASEAN Countries

2019 ◽  
pp. 103-114 ◽  
Author(s):  
Muhammad Shukri Che Lah ◽  
Nureize Arbaiy ◽  
Riswan Efendi
Keyword(s):  
Standard Deviation ◽  
Stock Market ◽  
Fuzzy Number ◽  
Triangular Fuzzy Number ◽  
Forecasting Model ◽  
Model Based ◽  
Stock Market Forecasting ◽  
Asean Countries

New Fuzzy Multi-Level Comprehensive Methodology for Safety Risk of Subway Operation

2013 ◽  
Vol 373-375 ◽  
pp. 2195-2199 ◽  
Author(s):  
Jia Yang Li ◽  
Kai Ning Liu ◽  
Ying Qiu Gu
Keyword(s):  
Standard Deviation ◽  
Public Transportation ◽  
Fuzzy Number ◽  
Integrated Approach ◽  
Triangular Fuzzy Number ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Multi Level ◽  
Safety Operation ◽  
Hierarchy Process

Subway has already become an important tool of public transportation. Subway security operation is to achieve a serious guarantee of safety and convenience. In this article, subway safety operation evaluation index system is constructed. Based on hierarchy process, triangular fuzzy number was used to describe the evaluation value obtained by experts. In order to be more objective and more precise, the correlation coefficient and standard deviation integrated approach will be used to calculate the weights of criteria in the fuzzy hierarchy process.

Research on the Application of Power Enterprises' SCMM Model Based on Sets-Valued

2014 ◽  
Vol 644-650 ◽  
pp. 6265-6268
Author(s):  
Ya Qing Deng ◽  
Hai Song Wang ◽  
Jian Qing Liu
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Information Flow ◽  
Fuzzy Number ◽  
Evaluation System ◽  
Market Competition ◽  
Triangular Fuzzy Number ◽  
Capital Flow ◽  
Chain Management ◽  
Model Based

With the market competition increasing of power enterprises, strengthen the supply chain management become more and more important. It has an important guiding significance to enhance the level and effect of supply chain management that study the issue of supply chain management and evaluation by using supply chain management maturity (SCMM). This paper makes a combination of supply chain management methods and the particularity of power enterprises, and using the theory of SCMM, to build a SCMM level evaluation system from three aspects of logistics, information flow and capital flow. On this basis, we use a method that combines set-valued statistics with triangular fuzzy number Lo evaluate. Finally, it proves that the SCMM level evaluation system is scientific and rational through examples study.

scholarly journals STRUCTURAL FUZZY RELIABILITY ANALYSIS USING THE CLASSICAL RELIABILITY THEORY

2021 ◽  
pp. 1-9
Author(s):  
Tuan Nguyen ◽  
Huynh Xuan Le
Keyword(s):  
Standard Deviation ◽  
Reliability Analysis ◽  
Fuzzy Number ◽  
Structural Reliability ◽  
Original Problem ◽  
Triangular Fuzzy Number ◽  
Reliability Theory ◽  
Design Codes ◽  
Fuzzy Reliability ◽  
Novel Approach

This study is focused  on a novel approach for calculating structural fuzzy reliability by using the classical reliability theory. In order to handle the structural fuzzy reliability problem, the formulas for establishing normal random variables equivalent to symmetric triangular fuzzy number are presented. From these equivalent random ones, the original problem is converted to the basic structural reliability problems, then the methods of the classical reliability theory should be applied to calculate. Moreover, this study proposes two notions in terms of central fuzzy reliability and standard deviation of fuzzy reliability as well as a calculation procedure to define them. Lastly, the ultimate fuzzy reliability of the proposed method is established and utilized to compare the allowable reliability in the design codes. Numerical results are supervised to verify the accuracy of the proposed method.

An integrated stock market forecasting model using neural networks

2008 ◽  
Vol 1 (1) ◽  
pp. 30 ◽  
Author(s):  
Gary R. Weckman ◽  
Sriram Lakshminarayanan ◽  
Jon H. Marvel ◽  
Andy Snow
Keyword(s):  
Neural Networks ◽  
Stock Market ◽  
Forecasting Model ◽  
Stock Market Forecasting

scholarly journals Forecasting ASEAN countries exchange rates using auto regression model based on triangular fuzzy number

2019 ◽  
Vol 14 (3) ◽  
pp. 1525
Author(s):  
Hamijah Mohd Rahman ◽  
Nureize Arbaiy ◽  
Riswan Efendi ◽  
Chuah Chai Wen
Keyword(s):  
Exchange Rate ◽  
Regression Model ◽  
Exchange Rates ◽  
Fuzzy Number ◽  
Early Stage ◽  
Single Point ◽  
Triangular Fuzzy Number ◽  
Data Preparation ◽  
Asean Countries ◽  
Auto Regression

<p>Exchange rate forecasting is important to represent the expectation of exchange rates future values. The forecasting task is due to the economic factor and the historical data used to forecast are exposed to uncertainty and observational error during data collection. The existing auto regression model only deals with uncertainty exist in the model, not in the data preparation. Uncertainties may contained in the data input and should be treated during data preparation which is an early stage of forecasting process. To date, only few researches discuss intensely on a fuzzy data preparation. However, data treatment during data preparation is important to reduce model’s error due to uncertainty problem. Hence, this paper presents an approach to construct Triangular Fuzzy Number to handle uncertainty in data during data preparation. As the Triangular Fuzzy Number is often used to represent uncertain information in a form of interval, this study proposed a procedure to construct Triangular Fuzzy Number from single point data. In this study, the Triangular Fuzzy Number is built in a form of symmetric triangular with 1%, 3% and 5% spread value. Autoregressive model is then used to forecast the exchange rate of Association of South East Asian Nation (ASEAN) countries. The result in this study shows that the forecasting exchange rate is significantly important to trace the movement of ASEAN countries exchange rates and beneficial in forecasting planning.</p>

scholarly journals Stock Market Forecasting from Multi-Source Data using Tolerance Based Multi-Agent Deep Reinforcement Learning

2020 ◽  
Vol 9 (3) ◽  
pp. 3492-3499
Keyword(s):  
Reinforcement Learning ◽  
Stock Market ◽  
Stock Exchange ◽  
Event Extraction ◽  
Forecasting Model ◽  
Future Trends ◽  
Forecasting Accuracy ◽  
Stock Market Forecasting ◽  
Source Data ◽  
Multi Agent

Analyzing and forecasting the future trends in stock market is challenging due to the ever increasing size of stock data. Modern techniques extract the stock indicators from the web data to forecast the stock movements. However, most previous studies were based on single source of data for extracting these indicators. This might not be effective in obtaining all the possible diverse factors that influence the market movements. Multi-source data has been rarely applied for stock prediction and even those techniques have limitations in handling larger data. In an attempt to utilize multi-source data more effectively for extracting stock indicators and improve the forecasting accuracy of stock movements, this paper developed a stock market forecasting model using Tolerance based Multi-Agent Deep Reinforcement Learning (TMA-DRL) model. The TMA-DRL model effectively combines the quantitative stock data with the indicators i.e. the events extracted from news data and sentiments extracted from tweets. This forecasting model utilizes Random forests to extract the twitter opinions and Restricted Boltzmann Machine (RBM) for event extraction from news data. Combining these indicators, the TMA-DRL model leads to improved data learning and provides highly accurate prediction of future stock trends. Datasets for evaluation were collected from three sources namely Twitter, Market News and Stock exchange, for 12 months period. Evaluation results illustrate the effectiveness of the proposed TMA-DRL stock market forecasting model which makes predictions with high accuracy and less time complexity.

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