scholarly journals Experiments with Fuzzy Methods for Forecasting Time Series as Alternatives to Classical Methods

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2517
Author(s):  
Bogdan Oancea ◽  
Richard Pospíšil ◽  
Marius Nicolae Jula ◽  
Cosmin-Ionuț Imbrișcă
Keyword(s):  
Time Series ◽  
Prediction Method ◽  
Fuzzy Time Series ◽  
New Techniques ◽  
Fuzzy Methods ◽  
Forecasting Methods ◽  
Different Types ◽  
Simple Question ◽  
Simple Exponential Smoothing ◽  
Economic Forecasts

Even though forecasting methods have advanced in the last few decades, economists still face a simple question: which prediction method gives the most accurate results? Econometric forecasting methods can deal with different types of time series and have good results, but in specific cases, they may fail to provide accurate predictions. Recently, new techniques borrowed from the soft computing area were adopted for economic forecasting. Starting from the importance of economic forecasts, we present an experimental study where we compared the accuracy of some of the most used econometric forecasting methods, namely the simple exponential smoothing, Holt and ARIMA methods, with that of two new methods based on the concept of fuzzy time series. We used a set of time series extracted from the Eurostat database and the R software for all data processing. The results of the experiments show that despite not being fully superior to the econometric techniques, the fuzzy time series forecasting methods could be considered as an alternative for specific time series.

Data-driven prediction method for characteristics of voltage sag based on fuzzy time series

2022 ◽  
Vol 134 ◽  
pp. 107394
Author(s):  
Ying Wang ◽  
Min-hui Yang ◽  
Hua-ying Zhang ◽  
Xian Wu ◽  
Wen-xi Hu
Keyword(s):  
Time Series ◽  
Prediction Method ◽  
Fuzzy Time Series ◽  
Data Driven ◽  
Voltage Sag

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 KOMPARASI KINERJA FUZZY TIME SERIES DENGAN MODEL RANTAI MARKOV DALAM MERAMALKAN PRODUK DOMESTIK REGIONAL BRUTO BALI

2014 ◽  
Vol 3 (3) ◽  
pp. 116
Author(s):  
I MADE ARYA ANTARA ◽  
I PUTU EKA N. KENCANA ◽  
I KOMANG GDE SUKARSA
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Error Rate ◽  
Original Data ◽  
Rate Of Change ◽  
Fuzzy Time Series ◽  
Domestic Product ◽  
Quarterly Data ◽  
Forecasting Methods ◽  
Soft Modeling

This paper aimed to elaborates and compares the performance of Fuzzy Time Series (FTS) model with Markov Chain (MC) model in forecasting the Gross Regional Domestic Product (GDRP) of Bali Province.  Both methods were considered as forecasting methods in soft modeling domain.  The data used was quarterly data of Bali’s GDRP for year 1992 through 2013 from Indonesian Bureau of Statistic at Denpasar Office.  Inspite of using the original data, rate of change from two consecutive quarters was used to model. From the in-sample forecasting conducted, we got the Average Forecas­ting Error Rate (AFER) for FTS dan MC models as much as 0,78 percent and 2,74 percent, respec­tively.  Based-on these findings, FTS outperformed MC in in-sample forecasting for GDRP of Bali’s data.

scholarly journals A New Method for Short Multivariate Fuzzy Time Series Based on Genetic Algorithm and Fuzzy Clustering

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Kamal S. Selim ◽  
Gihan A. Elanany
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Historical Data ◽  
New Method ◽  
Fuzzy Time Series ◽  
Forecasting Accuracy ◽  
Forecasting Methods ◽  
Critical Issues

Forecasting activities play an important role in our daily life. In recent years, fuzzy time series (FTS) methods were developed to deal with forecasting problems. FTS attracted researchers because of its ability to predict the future values in some critical situations where most standard forecasting models are doubtfully applicable or produce bad fittings. However, some critical issues in FTS are still open; these issues are often subjective and affect the accuracy of forecasting. In this paper, we focus on improving the accuracy of FTS forecasting methods. The new method integrates the fuzzy clustering and genetic algorithm with FTS to reduce subjectivity and improve its accuracy. In the new method, the genetic algorithm is responsible for selecting the proper model. Also, the fuzzy clustering algorithm is responsible for fuzzifying the historical data, based on its membership degrees to each cluster, and using these memberships to defuzzify the results. This method provides better forecasting accuracy when compared with other extant researches.

scholarly journals Pythagorean fuzzy time series model based on Pythagorean fuzzy c-means and improved Markov weighted in the prediction of the new COVID-19 cases

2021 ◽  
Author(s):  
Sidong Xian ◽  
Yue Cheng
Keyword(s):  
Time Series ◽  
Fuzzy Theory ◽  
Prediction Method ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
University Of Alabama ◽  
Fuzzy C Means ◽  
Forecasting Method ◽  
Fuzzy C Means Clustering ◽  
Realistic Prediction

Abstract Time series is an extremely important branch of prediction and the research on it plays an important guiding role in production and life. To get more realistic prediction results, scholars have explored the combination of fuzzy theory and time series. Although some results have been achieved so far, there are still gaps in the combination of n-Pythagorean fuzzy sets and time series. In this paper, a pioneering n-Pythagorean fuzzy time series model (n-PFTS) and its forecasting method (n-IMWPFCM) is proposed to employ a n-Pythagorean fuzzy c-means clustering method (n-PFCM) to overcome the subjectivity of directly assigning membership and non-membership values, thus improving the accuracy of the partition the universe of discoure. A novel improved Markov prediction method is exploited to enhance the prediction accuracy of the model. The proposed prediction method is applied to the yearly University of Alabama enrollments data and the new COVID-19 cases data. The results show that compared with the traditional fuzzy time series forecasting method, the proposed method has better forecasting accuracy. Meanwhile, it has the characteristics of low computational complexity and high interpretability and demonstrates the superiority of this model from a realistic perspective.

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