Non-Probabilistic Inverse Fuzzy Model in Time Series Forecasting

2018 ◽  
Vol 26 (05) ◽  
pp. 855-873 ◽  
Author(s):  
Riswan Efendi ◽  
Mustafa Mat Deris
Keyword(s):  
Time Series ◽  
Fuzzy Model ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Forecasting Accuracy ◽  
Fuzzy Function ◽  
Car Accidents ◽  
Proposed Model ◽  
Enrollment Data

Many models and techniques have been proposed by researchers to improve forecasting accuracy using fuzzy time series. However, very few studies have tackled problems that involve inverse fuzzy function into fuzzy time series forecasting. In this paper, we modify inverse fuzzy function by considering new factor value in establishing the forecasting model without any probabilistic approaches. The proposed model was evaluated by comparing its performance with inverse and noninverse fuzzy time series models in forecasting the yearly enrollment data of several universities, such as Alabama University, Universiti Teknologi Malaysia (UTM), and QiongZhou University; the yearly car accidents in Belgium; and the monthly Turkish spot gold price. The results suggest that the proposed model has potential to improve the forecasting accuracy compared to the existing inverse and non-inverse fuzzy time series models. This paper contributes to providing the better future forecast values using the systematic rules.

Enhanced Forecasting Accuracy of Fuzzy Time Series Model Based on Combined Fuzzy C-Mean Clustering with Particle Swam Optimization

2020 ◽  
Vol 19 (02) ◽  
pp. 2050017
Author(s):  
Nghiem Van Tinh
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Pso Algorithm ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Forecasting Accuracy ◽  
Forecasting Models ◽  
Proposed Model ◽  
Fuzzy C Means Clustering ◽  
Universe Of Discourse

Over the past 25 years, numerous fuzzy time series forecasting models have been proposed to deal the complex and uncertain problems. The main factors that affect the forecasting results of these models are partition universe of discourse, creation of fuzzy relationship groups and defuzzification of forecasting output values. So, this study presents a hybrid fuzzy time series forecasting model combined particle swarm optimization (PSO) and fuzzy C-means clustering (FCM) for solving issues above. The FCM clustering is used to divide the historical data into initial intervals with unequal size. After generating interval, the historical data is fuzzified into fuzzy sets with the aim to serve for establishing fuzzy relationship groups according to chronological order. Then the information obtained from the fuzzy relationship groups can be used to calculate forecasted value based on a new defuzzification technique. In addition, in order to enhance forecasting accuracy, the PSO algorithm is used for finding optimum interval lengths in the universe of discourse. The proposed model is applied to forecast three well-known numerical datasets (enrolments data of the University of Alabama, the Taiwan futures exchange —TAIFEX data and yearly deaths in car road accidents in Belgium). These datasets are also examined by using some other forecasting models available in the literature. The forecasting results obtained from the proposed model are compared to those produced by the other models. It is observed that the proposed model achieves higher forecasting accuracy than its counterparts for both first—order and high—order fuzzy logical relationship.

scholarly journals ENROLLMENT FORECASTING BASED ON LINGUISTIC TIME SERIES

2020 ◽  
Vol 36 (2) ◽  
pp. 119-137
Author(s):  
Nguyen Duy Hieu ◽  
Nguyen Cat Ho ◽  
Vu Nhu Lan
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Time Series Data ◽  
Historical Data ◽  
Fuzzy Model ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Forecasting Model ◽  
Proposed Model

Dealing with the time series forecasting problem attracts much attention from the fuzzy community. Many models and methods have been proposed in the literature since the publication of the study by Song and Chissom in 1993, in which they proposed fuzzy time series together with its fuzzy forecasting model for time series data and the fuzzy formalism to handle their uncertainty. Unfortunately, the proposed method to calculate this fuzzy model was very complex. Then, in 1996, Chen proposed an efficient method to reduce the computational complexity of the mentioned formalism. Hwang et al. in 1998 proposed a new fuzzy time series forecasting model, which deals with the variations of historical data instead of these historical data themselves. Though fuzzy sets are concepts inspired by fuzzy linguistic information, there is no formal bridge to connect the fuzzy sets and the inherent quantitative semantics of linguistic words. This study proposes the so-called linguistic time series, in which words with their own semantics are used instead of fuzzy sets. By this, forecasting linguistic logical relationships can be established based on the time series variations and this is clearly useful for human users. The effect of the proposed model is justified by applying the proposed model to forecast student enrollment historical data.

High Order Time Series Forecasting Using Fuzzy Discretization

2018 ◽  
pp. 1655-1674
Author(s):  
Mahua Bose ◽  
Kalyani Mali
Keyword(s):  
Time Series ◽  
Stock Price ◽  
Positional Information ◽  
Time Series Forecasting ◽  
High Order ◽  
Fuzzy Time Series ◽  
Production Data ◽  
Forecasting Accuracy ◽  
High Order Time ◽  
Enrollment Data

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.

An Efficient Time Series Forecasting Method Exploiting Fuzziness and Turbulences in Data

2017 ◽  
Vol 6 (4) ◽  
pp. 83-98 ◽  
Author(s):  
Prateek Pandey ◽  
Shishir Kumar ◽  
Sandeep Shrivastava
Keyword(s):  
Time Series ◽  
Performance Comparison ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Data Set ◽  
Common Trend ◽  
Proposed Model ◽  
Forecasting Method ◽  
High Degree

In recent years, there has been a growing interest in Time Series forecasting. A number of time series forecasting methods have been proposed by various researchers. However, a common trend found in these methods is that they all underperform on a data set that exhibit uneven ups and downs (turbulences). In this paper, a new method based on fuzzy time-series (henceforth FTS) to forecast on the fundament of turbulences in the data set is proposed. The results show that the turbulence based fuzzy time series forecasting is effective, especially, when the available data indicate a high degree of instability. A few benchmark FTS methods are identified from the literature, their limitations and gaps are discussed and it is observed that the proposed method successfully overcome their deficiencies to produce better results. In order to validate the proposed model, a performance comparison with various conventional time series models is also presented.

An Efficient Time Series Forecasting Method Exploiting Fuzziness and Turbulences in Data

2018 ◽  
pp. 1773-1791 ◽  
Author(s):  
Prateek Pandey ◽  
Shishir Kumar ◽  
Sandeep Shrivastava
Keyword(s):  
Time Series ◽  
Performance Comparison ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Data Set ◽  
Common Trend ◽  
Proposed Model ◽  
Forecasting Method ◽  
High Degree ◽  
Degree Of Instability

In recent years, there has been a growing interest in Time Series forecasting. A number of time series forecasting methods have been proposed by various researchers. However, a common trend found in these methods is that they all underperform on a data set that exhibit uneven ups and downs (turbulences). In this paper, a new method based on fuzzy time-series (henceforth FTS) to forecast on the fundament of turbulences in the data set is proposed. The results show that the turbulence based fuzzy time series forecasting is effective, especially, when the available data indicate a high degree of instability. A few benchmark FTS methods are identified from the literature, their limitations and gaps are discussed and it is observed that the proposed method successfully overcome their deficiencies to produce better results. In order to validate the proposed model, a performance comparison with various conventional time series models is also presented.

scholarly journals A Novel Fuzzy Time Series Forecasting Model Based on Multiple Linear Regression and Time Series Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yanpeng Zhang ◽  
Hua Qu ◽  
Weipeng Wang ◽  
Jihong Zhao
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Time Series Forecasting ◽  
High Order ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Time Series Clustering ◽  
Proposed Model ◽  
High Order Time

Time series forecasting models based on a linear relationship model show great performance. However, these models cannot handle the the data that are incomplete, imprecise, and ambiguous as the interval-based fuzzy time series models since the process of fuzzification is abandoned. This article proposes a novel fuzzy time series forecasting model based on multiple linear regression and time series clustering for forecasting market prices. The proposed model employs a preprocessing to transform the set of fuzzy high-order time series into a set of high-order time series, with synthetic minority oversampling technique. After that, a high-order time series clustering algorithm based on the multiple linear regression model is proposed to cluster dataset of fuzzy time series and to build the linear regression model for each cluster. Then, we make forecasting by calculating the weighted sum of linear regression models’ results. Also, a learning algorithm is proposed to train the whole model, which applies artificial neural network to learn the weights of linear models. The interval-based fuzzification ensures the capability to deal with the uncertainties, and linear model and artificial neural network enable the proposed model to learn both of linear and nonlinear characteristics. The experiment results show that the proposed model improves the average forecasting accuracy rate and is more suitable for dealing with these uncertainties.

scholarly journals Fuzzy Time Series Forecasting Model based on Frequency Density and Similarity Measure Approach

2018 ◽  
Vol 7 (4.30) ◽  
pp. 281
Author(s):  
Nazirah Ramli ◽  
Siti Musleha Ab Mutalib ◽  
Daud Mohamad
Keyword(s):  
Time Series ◽  
Similarity Measure ◽  
Fuzzy Numbers ◽  
Historical Data ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Forecasting Accuracy ◽  
Forecasting Error ◽  
Proposed Model ◽  
Fuzzy Forecast

This paper proposes an enhanced fuzzy time series (FTS) prediction model that can keep some information under a various level of confidence throughout the forecasting procedure. The forecasting accuracy is developed based on the similarity between the fuzzified historical data and the fuzzy forecast values. No defuzzification process involves in the proposed method. The frequency density method is used to partition the interval, and the area and height type of similarity measure is utilized to get the forecasting accuracy. The proposed model is applied in a numerical example of the unemployment rate in Malaysia. The results show that on average 96.9% of the forecast values are similar to the historical data. The forecasting error based on the distance of the similarity measure is 0.031. The forecasting accuracy can be obtained directly from the forecast values of trapezoidal fuzzy numbers form without experiencing the defuzzification procedure.

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 ỨNG DỤNG ĐẠI SỐ GIA TỬ TRONG DỰ BÁO CHUỖI THỜI GIAN MỜ

2016 ◽  
Vol 54 (2) ◽  
pp. 161
Author(s):  
Nguyễn Cát Hồ ◽  
Nguyễn Công Điều ◽  
Vũ Như Lân
Keyword(s):  
Time Series ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Fuzzy Approach ◽  
University Of Alabama ◽  
Simple Arithmetic ◽  
Proposed Model ◽  
Improving Accuracy ◽  
The University ◽  
And Control

Fuzzy time series given by Song & Chissom (1993) in magazine "Fuzzy Sets and   Systems" has been widely studied for forecasting purposes. However, the accuracy of forecasts based on the concept of fuzzy approach of Song & Chissom is not high because of such depends on many factors. Chen (1996) proposed an efficient fuzzy time series model which consists of simple arithmetic calculations only. After that, this has been widely studied for improving accuracy of forecasting in many applications to get better results. The hedge algebras developed by Nguyen and Wechler (1990) was completely different from the fuzzy approach. Here the hedge algebras was used to model  linguistic domains and variables and their semantic structure is obtained. Instead of performing fuzzification and defuzzification, more simple methods are adopted, termed as semantization and desemantization, respectively. The hedge algebras based fuzzy system is a new topic, which was first applied to fuzzy control 2008 [16]. Hedge algebras applications for some specific problems in the field of information technology and control has a number of important results and confirm advantages of this approach in comparing with fuzzy approach. In continuilty of hedge algebras applications, this paper is mainly focused on the field of  fuzzy time series forecasting under hedge algebras approach. In this paper, we present a new approach using hedge algebras to provide a computational model, which is completely different from the fuzzy approach for fuzzy time series forecasting. The experimental results of forecasting enrollments of students of the University of Alabama show that the model of fuzzy time series based on hedge algebras is better than many existing models. We can see that the proposed model gains higher forecasting accuracy than the original model presented by Song and Chissom (1993b), Chen (1996, 2002), or Lee (2009), Qiu (2011), Egrioglu (2012), Ozdemir ( 2012) and Uslu (2013).

scholarly journals Fuzzy Local Trend Transform based Fuzzy Time Series Forecasting Model

2011 ◽  
Vol 6 (4) ◽  
pp. 603 ◽  
Author(s):  
Jingpei Dan ◽  
Fangyan Dong ◽  
Kaoru Hirota
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Forecast Accuracy ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Proposed Model ◽  
Trend Variation ◽  
Conventional Models ◽  
Local Trend

A fuzzy local trend transform based fuzzy time series forecasting model is proposed to improve practicability and forecast accuracy by providing forecast of local trend variation based on the linguistic representation of ratios between any two consecutive points in original time series. Local trend variation satisfies a wide range of real applications for the forecast, the practicability is thereby improved. Specific values based on the forecasted local trend variations that reflect fluctuations in historical data are calculated accordingly to enhance the forecast accuracy. Compared with conventional models, the proposed model is validated by about 50% and 60% average improvement in terms of MLTE (mean local trend error) and RMSE (root mean squared error), respectively, for three typical forecasting applications. The MLTE results indicate that the proposed model outperforms conventional models significantly in reflecting fluctuations in historical data, and the improved RMSE results confirm an inherent enhancement of reflection of fluctuations in historical data and hence a better forecast accuracy. The potential applications of the proposed fuzzy local trend transform include time series clustering, classification, and indexing.

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