A Score Function-Based Method of Forecasting Using Intuitionistic Fuzzy Time Series

2018 ◽  
Vol 14 (01) ◽  
pp. 91-111 ◽  
Author(s):  
Abhishekh ◽  
Surendra Singh Gautam ◽  
S. R. Singh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Stock Exchange ◽  
Market Price ◽  
Intuitionistic Fuzzy Set ◽  
Score Function ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy

Intuitionistic fuzzy set plays a vital role in data analysis and decision-making problems. In this paper, we propose an enhanced and versatile method of forecasting using the concept of intuitionistic fuzzy time series (FTS) based on their score function. The developed method has been presented in the form of simple computational steps of forecasting instead of complicated max–min compositions operator of intuitionistic fuzzy sets to compute the relational matrix [Formula: see text]. Also, the proposed method is based on the maximum score and minimum accuracy function of intuitionistic fuzzy numbers (IFNs) to fuzzify the historical time series data. Further intuitionistic fuzzy logical relationship groups are defined and also provide a forecasted value and lies in an interval and is more appropriate rather than a crisp value. Furthermore, the proposed method has been implemented on the historical student enrollments data of University of Alabama and obtains the forecasted values which have been compared with the existing methods to show its superiority. The suitability of the proposed model has also been examined to forecast the movement of share market price of State Bank of India (SBI) at Bombay Stock Exchange (BSE). The results of the comparison of MSE and MAPE indicate that the proposed method produces more accurate forecasting results.

A fuzzy time series forecasting method induced by intuitionistic fuzzy sets

2015 ◽  
Vol 06 (04) ◽  
pp. 1550041 ◽  
Author(s):  
Sanjay Kumar ◽  
Sukhdev Singh Gangwar
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Time Series Data ◽  
Financial Time Series ◽  
Intuitionistic Fuzzy Sets ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Intuitionistic Fuzzy ◽  
Forecasting Method

Intuitionistic fuzzy sets (IFSs) are well established as a tool to handle the hesitation in the decision system. In this research paper, fuzzy sets induced by IFS are used to develop a fuzzy time series forecasting model to incorporate degree of hesitation (nondeterminacy). To improve the forecasting accuracy, induced fuzzy sets are used to establish fuzzy logical relations. To verify the performance of the proposed model, it is implemented on one of the benchmarking time series data. Further, developed forecasting method is also tested and validated by applying it on a financial time series data. In order to show the accuracy in forecasting, the method is compared with other forecasting methods using different error measures.

Intuitionistic Fuzzy Time Series Forecasting Based on Dual Hesitant Fuzzy Set for Stock Market

2019 ◽  
pp. 37-57
Author(s):  
Sanjay Kumar ◽  
Kamlesh Bisht ◽  
Krishna Kumar Gupta
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Time Series Data ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Hesitant Fuzzy Set ◽  
Series Data ◽  
Intuitionistic Fuzzy ◽  
Dual Hesitant Fuzzy Set

In this chapter, an application of dual hesitant fuzzy set (DHFS) in intuitionistic fuzzy time series forecasting is proposed to handle fuzziness and non-determinism that occurs due to multiple valid fuzzification method for time series data. Advantages of the proposed DHFS-based time series forecasting method are that it includes characteristics of both intuitionistic and hesitant fuzzy sets to handle the non-determinism and hesitancy corresponding to single membership grade multiple membership grades of an element. In the present study, universe of discourse is partitioned and fuzzified the time series data by two different fuzzification methods (triangular and Gaussian) to construct DHFS. Further, elements of DHFS are aggregated to construct the intuitionistic fuzzy sets. Proposed method is implemented over the share market prizes of SBI at BSE, India and SENSEX of BSE to confirm its out performance over existing time series forecasting methods using RMSE and AFER.

Fuzzy Time Series Model Based on Intuitionistic Fuzzy Sets for Empirical Research in Stock Market

2012 ◽  
Vol 3 (4) ◽  
pp. 71-84 ◽  
Author(s):  
Bhagawati P. Joshi ◽  
Sanjay Kumar
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Stock Exchange ◽  
Intuitionistic Fuzzy Set ◽  
Intuitionistic Fuzzy Sets ◽  
Computational Approach ◽  
Computational Method ◽  
Fuzzy Time Series ◽  
Market Prices ◽  
Intuitionistic Fuzzy

Intuitionistic fuzzy sets introduced by Atanassov are generalization of fuzzy sets as they also handle the non-determinacy which is caused by degree of hesitation of decision maker. The present study proposes a computational method of forecasting for fuzzy time series. In the proposed method the notion of intuitionistic fuzzy set is used in fuzzy time series forecasting with simplified computational approach. The developed model has been tested on the movement of share market prices of State Bank of India (SBI) at Bombay Stock Exchange (BSE), India. Further the method has been implemented for forecasting SENSEX of BSE. The suitability of the developed model has also been examined by comparing it with the other existing models to show its superiority.

Multi-Factor Fuzzy Time Series Model Based on Stock Volatility for Forecasting Taiwan Stock Index

2011 ◽  
Vol 211-212 ◽  
pp. 1119-1123 ◽  
Author(s):  
Ching Hsue Cheng ◽  
Jing Wei Liu ◽  
Tzu Hsuan Lin
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Stock Exchange ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
Series Data ◽  
Stock Volatility ◽  
Taiwan Stock Exchange ◽  
Proposed Model

Fuzzy time series have in recent years drawn many scholars' attention due to their ability can handle the time series data with incomplete, imprecise and ambiguous pattern. However, most traditional time series models employed only single variable (stock index) in forecasting, yet ignored some factors that would also affect the stock volatility. Therefore, this paper proposes a novel forecasting model using multi-factor fuzzy time series model to forecast TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock index). Multi-factor fuzzy time series model is composed of three main components: stock index, trading volume and interactions between two stock markets. In order to evaluate the performance of the proposed model, the transaction records of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock index) and NASDAQ(National Association of Securities Dealers Automated Quotations) from 2000/01/04 to 2003/12/31 are used as experimental dataset and the root mean square error (RMSE) as evaluation criterion. The results show that the proposed model outperforms the listing models in accuracy for forecasting Taiwan stock market.

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.

scholarly journals Peran Grup Afiliasi dalam Memoderasi Pengaruh Hutang terhadap Nilai Perusahaan pada Perusahaan Property & Real Estate yang terdaftar di Bursa Efek Indonesia

FORUM EKONOMI ◽  
2017 ◽  
Vol 19 (1) ◽  
pp. 58
Author(s):  
Fina Maulidyani ◽  
Set Asmapane ◽  
Ledy Setiawati
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Time Series Data ◽  
Stock Exchange ◽  
Debt Financing ◽  
Series Data ◽  
Simple Linear Regression ◽  
Group Affiliation ◽  
A Company ◽  
Positive Effect

The objective of this research is to examine empirically the effect of Debt on Firm’s Value and the ability of Group Affiliation to moderate the effect of Debt toward Firm’s Value by using an approach simple linear regression and Moderated Regression Analysis (MRA). This research uses time series data in 2011 – 2015 for company's finance data that got from Indonesia Stock Exchange. Election Procedure sample uses purposive sampling and the result are existed 35 companies that fulfill criterion. The results show that Debt has positive effect on Firm’s Value, while Group Affiliation act as a moderating variable can reduce the effect of Debt toward Firm’s Value. The higher capability of group affiliation to take control of a company, the lower debt financing that company has.Keywords:     Debt Financing, Firm’s Value, Group Affiliation

scholarly journals A Predictive Meta Model for Forecasting Stock Price using Time Series Data

2021 ◽  
Vol 10 (1) ◽  
pp. 345-356
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Price ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Meta Model ◽  
Price Range ◽  
Stock Traders ◽  
The Right

Stock market prediction through time series is a challenging as well as an interesting research areafor the finance domain, through which stock traders and investors can find the right time to buy/sell stocks. However, various algorithms have been developed based on the statistical approach to forecast the time series for stock data, but due to the volatile nature and different price ranges of the stock price one particular algorithm is not enough to visualize the prediction. This study aims to propose a model that will choose the preeminent algorithm for that particular company’s stock that can forecastthe time series with minimal error. This model can assist a trader/investor with or without expertise in the stock market to achieve profitable investments. We have used the Stock data from Stock Exchange Bangladesh, which covers 300+ companies to train and test our system. We have classified those companies based on the stock price range and then applied our model to identify which algorithm suites most for a particular range of stock price. Comparative forecasting results of all algorithms in diverse price ranges have been presented to show the usefulness of this Predictive Meta Model

An Efficient Method for Forecasting Using Fuzzy Time Series

2017 ◽  
pp. 287-304 ◽  
Author(s):  
Pritpal Singh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Weather Forecasting ◽  
Small Error ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Data Sets ◽  
Proposed Model ◽  
Temperature Forecasting ◽  
The University

Forecasting using fuzzy time series has been applied in several areas including forecasting university enrollments, sales, road accidents, financial forecasting, weather forecasting, etc. Recently, many researchers have paid attention to apply fuzzy time series in time series forecasting problems. In this paper, we present a new model to forecast the enrollments in the University of Alabama and the daily average temperature in Taipei, based on one-factor fuzzy time series. In this model, a new frequency based clustering technique is employed for partitioning the time series data sets into different intervals. For defuzzification function, two new principles are also incorporated in this model. In case of enrollments as well daily temperature forecasting, proposed model exhibits very small error rate.

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