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

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.

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Non-Probabilistic Inverse Fuzzy Model in Time Series Forecasting

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488518500381 ◽

2018 ◽

Vol 26 (05) ◽

pp. 855-873 ◽

Cited By ~ 1

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.

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An improved fuzzy time series forecasting method using trapezoidal fuzzy numbers

Fuzzy Optimization and Decision Making ◽

10.1007/s10700-006-0025-9 ◽

2007 ◽

Vol 6 (1) ◽

pp. 63-80 ◽

Cited By ~ 47

Author(s):

Hao-Tien Liu

Keyword(s):

Time Series ◽

Fuzzy Numbers ◽

Time Series Forecasting ◽

Fuzzy Time Series ◽

Trapezoidal Fuzzy Numbers ◽

Forecasting Method

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Intuitionistic high-order fuzzy time series forecasting method based on pi-sigma artificial neural networks trained by artificial bee colony

Granular Computing ◽

10.1007/s41066-018-00143-5 ◽

2018 ◽

Vol 4 (4) ◽

pp. 639-654 ◽

Cited By ~ 6

Author(s):

Erol Egrioglu ◽

Ufuk Yolcu ◽

Eren Bas

Keyword(s):

Neural Networks ◽

Time Series ◽

Artificial Neural Networks ◽

Artificial Bee Colony ◽

Time Series Forecasting ◽

High Order ◽

Fuzzy Time Series ◽

Bee Colony ◽

Forecasting Method ◽

Artificial Neural

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

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026820500170 ◽

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.

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

Mathematical Problems in Engineering ◽

10.1155/2020/9546792 ◽

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.

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Stock index forecasting: A new fuzzy time series forecasting method

Journal of Forecasting ◽

10.1002/for.2734 ◽

2020 ◽

Author(s):

Hao Wu ◽

Haiming Long ◽

Yue Wang ◽

Yanqi Wang

Keyword(s):

Time Series ◽

Time Series Forecasting ◽

Fuzzy Time Series ◽

Stock Index ◽

Forecasting Method ◽

Stock Index Forecasting

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

Mathematical Problems in Engineering ◽

10.1155/2013/927394 ◽

2013 ◽

Vol 2013 ◽

pp. 1-11 ◽

Cited By ~ 3

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.

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