scholarly journals The Effect of the Length of Interval in Fuzzy Time Series Models on Forecasting

2012 ◽  
pp. 64-77
Keyword(s):  
Time Series ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Length Of Interval

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.

Fuzzy Time Series Models for LNSZZS Forecasting

2012 ◽  
Vol 7 (19) ◽  
pp. 470-478 ◽  
Author(s):  
Sun Baiqing ◽  
Xiong Shan ◽  
Wu Berlin
Keyword(s):  
Time Series ◽  
Fuzzy Time Series ◽  
Time Series Models

scholarly journals High-Order Fuzzy Time Series Model Based on Generalized Fuzzy Logical Relationship

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Wangren Qiu ◽  
Xiaodong Liu ◽  
Hailin Li
Keyword(s):  
Time Series ◽  
Stock Exchange ◽  
Composite Index ◽  
High Order ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Logical Relationship ◽  
Proposed Model ◽  
The University ◽  
Fuzzy Logical

In view of techniques for constructing high-order fuzzy time series models, there are three methods which are based on advanced algorithms, computational methods, and grouping the fuzzy logical relationships, respectively. The last kind model has been widely applied and researched for the reason that it is easy to be understood by the decision makers. To improve the fuzzy time series forecasting model, this paper presents a novel high-order fuzzy time series models denoted asGTS(M,N)on the basis of generalized fuzzy logical relationships. Firstly, the paper introduces some concepts of the generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the proposed model is implemented in forecasting enrollments of the University of Alabama. As an example of in-depth research, the proposed approach is also applied to forecast the close price of Shanghai Stock Exchange Composite Index. Finally, the effects of the number of orders and hierarchies of fuzzy logical relationships on the forecasting results are discussed.

scholarly journals Forecasting the COVID-19 Outbreak: An Application of Arima and Fuzzy Time Series Models

2020 ◽  
Author(s):  
Prashant Verma ◽  
Mukti Khetan ◽  
Shikha Dwivedi ◽  
Shweta Dixit
Keyword(s):  
Time Series ◽  
Health Care Systems ◽  
Medical Personnel ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Data Set ◽  
Care Systems ◽  
Novel Coronavirus ◽  
Time Series Methodology ◽  
Near Future

Abstract Purpose: The whole world is surfaced with an inordinate challenge of mankind due to COVID-19, caused by 2019 novel coronavirus (SARS-CoV-2). After taking hundreds of thousands of lives, millions of people are still in the substantial grasp of this virus. This virus is highly contagious with reproduction number R0, as high as 6.5 worldwide and between 1.5 to 2.6 in India. So, the number of total infections and the number of deaths will get a day-to-day hike until the curve flattens. Under the current circumstances, it becomes inevitable to develop a model, which can anticipate future morbidities, recoveries, and deaths. Methods: We have developed some models based on ARIMA and FUZZY time series methodology for the forecasting of COVID-19 infections, mortalities and recoveries in India and Maharashtra explicitly, which is the most affected state in India, following the COVID-19 statistics till “Lockdown 3.0” (17th May 2020). Results: Both models suggest that there will be an exponential uplift in COVID-19 cases in the near future. We have forecasted the COVID-19 data set for next seven days. The forecasted values are in good agreement with real ones for all six COVID-19 scenarios for Maharashtra and India as a whole as well.Conclusion: The forecasts for the ARIMA and FUZZY time series models will be useful for the policymakers of the health care systems so that the system and the medical personnel can be prepared to combat the pandemic.

Application of fuzzy time series models for forecasting the amount of Taiwan export

2010 ◽  
Vol 37 (2) ◽  
pp. 1465-1470 ◽  
Author(s):  
Hsien-Lun Wong ◽  
Yi-Hsien Tu ◽  
Chi-Chen Wang
Keyword(s):  
Time Series ◽  
Fuzzy Time Series ◽  
Time Series Models

Fuzzy Time Series Models Using Pliant- and Asymptotically Pliant Arithmetic-Based Inference

2018 ◽  
Vol 52 (1) ◽  
pp. 21-55 ◽  
Author(s):  
József Dombi ◽  
Tamás Jónás ◽  
Zsuzsanna E. Tóth
Keyword(s):  
Time Series ◽  
Fuzzy Time Series ◽  
Time Series Models

Fuzzy Time Series

2017 ◽  
pp. 157-190
Author(s):  
Ali Karasan ◽  
İsmail Sevim ◽  
Melih Çinar
Keyword(s):  
Time Series ◽  
Performance Indicators ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Advantages And Disadvantages ◽  
Generic Strategies

In this chapter, we are planning to make a comparison between conventional Time Series Models and Fuzzy Time Series Models by an application in an e-commerce company. Future sales of furniture will be predicted. The performance of different models and forecasting periods are going to be analyzed to discuss advantages and disadvantages of each method. MAE is chosen as performance indicators of each model and forecasting period combination. As a conclusion to this chapter, generic strategies for prediction in an e-commerce company will be formulated in consideration of these indicators.

Fuzzy Multiple-Periods Time-Series Model Based on GSP and AR for Forecasting Gold Price

2011 ◽  
Vol 211-212 ◽  
pp. 1124-1128 ◽  
Author(s):  
Jing Wei Liu ◽  
Ching Hsue Cheng ◽  
Chung Ho Su ◽  
Ming Chien Tsai
Keyword(s):  
Time Series ◽  
Fuzzy Rule ◽  
Time Series Model ◽  
Gold Price ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Fuzzy Relations ◽  
Previous Time ◽  
Proposed Model ◽  
Comparison Results

In the recent years, traditional time series model has been widely researched. The previous time series methods can predict future problems based on historical data, but have a problem that determines subjectively the length of intervals. Song and Chissom[6-7]proposed the fuzzy time series to solve the problem of traditional time series methods. So far, many researchers have proposed different fuzzy time series models to deal with uncertain and vague data. Besides, the consideration of a forecasting stage only discusses the relations for previous period and next period. In addition, a shortcoming of previous time series models didn’t consider appropriately the weights of fuzzy relations. This study builds fuzzy rule based on association rules and compute the cardinality of each fuzzy relation. Then, calculating the weights of fuzzy relations solve above problems. Moreover, the proposed method is able to build the multiple periods fuzzy rules based on concept of large itemsets of Apriori. To verify the proposed model, the gold price datasets is employed as experimental datasets. This study compares the forecasting accuracy of proposed model with other methods, and the comparison results show that the proposed method has better performance than other methods.

High Order Time Series Forecasting using Fuzzy Discretization

2016 ◽  
Vol 5 (4) ◽  
pp. 147-164 ◽  
Author(s):  
Mahua Bose ◽  
Kalyani Mali
Keyword(s):  
Time Series ◽  
Stock Price ◽  
Positional Information ◽  
High Order ◽  
Fuzzy Time Series ◽  
Production Data ◽  
Forecasting Accuracy ◽  
High Order Time ◽  
Enrollment Data ◽  
Length Of Interval

In recent years, various methods for forecasting fuzzy time series have been presented in different areas, such as stock price, enrollments, weather, production etc. It is observed that in most of the cases, static length of intervals/equal length of interval has been used. Length of the interval has significant role on forecasting accuracy. The objective of this present study is to incorporate the idea of fuzzy discretization into interval creation and examine the effect of positional information of elements within a group or interval to the forecast. This idea outperforms the existing high order forecast methods using fixed interval. Experiments are carried on three datasets including Lahi production data, enrollment data and rainfall data which deal with a lot of uncertainty.

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