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 A NEW HYBRID FUZZY TIME SERIES FORECASTING MODEL BASED ON COMBINING FUZZY C-MEANS CLUSTERING AND PARTICLE SWAM OPTIMIZATION

2019 ◽  
Vol 35 (3) ◽  
pp. 267-292
Author(s):  
Nghiem Van Tinh ◽  
Nguyen Cong Dieu
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Historical Data ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Forecasting Accuracy ◽  
Fuzzy C Means ◽  
Model Based ◽  
Proposed Model ◽  
Fcm Clustering

Fuzzy time series (FTS) model is one of the effective tools that can be used to identify factors in order to solve the complex process and uncertainty. Nowadays, it has been widely used in many forecasting problems. However, establishing effective fuzzy relationships groups, finding proper length of each interval, and building defuzzification rule are three issues that exist in FTS model. Therefore, in this paper, a novel FTS forecasting model based on fuzzy C-means (FCM) clustering and particle swarm optimization (PSO) was developed to enhance the forecasting accuracy. Firstly, the FCM clustering is used to divide the historical data into intervals with different lengths. After generating interval, the historical data is fuzzified into fuzzy sets. Following, fuzzy relationship groups were established based on the appearance history of the fuzzy sets on the right-hand side of the fuzzy logical relationships with the aim to serve for calculating the forecasting output.  Finally, the proposed model combined with PSO algorithm was applied to adjust interval lengths and find proper intervals in the universe of discourse for obtaining the best forecasting accuracy. To verify the effectiveness of the forecasting model, three numerical datasets (enrolments data of the University of Alabama, the Taiwan futures exchange –TAIFEX data and yearly deaths in car road accidents in Belgium) are selected to illustrate the proposed model. The experimental results indicate that the proposed model is better than any existing forecasting models in term of forecasting accuracy based on the first – order and high-order FTS.

scholarly journals A NEW HYBRID FUZZY TIME SERIES FORECASTING MODEL BASED ON COMBINING FUZZY C-MEANS CLUSTERING AND PARTICLE SWAM OPTIMIZATION

2019 ◽  
Vol 35 (3) ◽  
pp. 267-292
Author(s):  
Nghiem Van Tinh ◽  
Nguyen Cong Dieu
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Historical Data ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Forecasting Accuracy ◽  
Fuzzy C Means ◽  
Model Based ◽  
Proposed Model ◽  
Fcm Clustering

Fuzzy time series (FTS) model is one of the effective tools that can be used to identify factors in order to solve the complex process and uncertainty. Nowadays, it has been widely used in many forecasting problems. However, establishing effective fuzzy relationships groups, finding proper length of each interval, and building defuzzification rule are three issues that exist in FTS model. Therefore, in this paper, a novel FTS forecasting model based on fuzzy C-means (FCM) clustering and particle swarm optimization (PSO) was developed to enhance the forecasting accuracy. Firstly, the FCM clustering is used to divide the historical data into intervals with different lengths. After generating interval, the historical data is fuzzified into fuzzy sets. Following, fuzzy relationship groups were established based on the appearance history of the fuzzy sets on the right-hand side of the fuzzy logical relationships with the aim to serve for calculating the forecasting output.  Finally, the proposed model combined with PSO algorithm was applied to adjust interval lengths and find proper intervals in the universe of discourse for obtaining the best forecasting accuracy. To verify the effectiveness of the forecasting model, three numerical datasets (enrolments data of the University of Alabama, the Taiwan futures exchange –TAIFEX data and yearly deaths in car road accidents in Belgium) are selected to illustrate the proposed model. The experimental results indicate that the proposed model is better than any existing forecasting models in term of forecasting accuracy based on the first – order and high-order FTS.

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 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.

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.

IMPROVING THE FORECASTED ACCURACY OF MODEL BASED ON FUZZY TIME SERIES AND K-MEANS CLUSTERING

2017 ◽  
Vol 3 (2) ◽  
pp. 46
Author(s):  
Nghiem Van Tinh ◽  
Nguyen Cong Dieu
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Historical Data ◽  
Fuzzy Time Series ◽  
Interval Length ◽  
Logical Relationship ◽  
Forecasting Accuracy ◽  
Model Based ◽  
Proposed Model ◽  
Fuzzy Logical

There are many approaches to improve the forecasted accuracy of model based on fuzzy time series such as: determining the optimal interval length, establishing fuzzy logic relationship groups, similarity measures, …wherein, the length of intervals is a factor that greatly affects forecasting results in fuzzy time series model. In this paper, a new forecasting model based on combining the fuzzy time series (FTS) and K-mean clustering algorithm with three computational methods, K-means clustering technique, the time - variant fuzzy logical relationship groups and defuzzification forecasting rules, is presented. Firstly, we apply the K-mean clustering algorithm to divide the historical data into clusters and tune them into intervals with proper lengths. Then, based on the new intervals obtained, the proposed method is used to fuzzify all the historical data and create the time -variant fuzzy logical relationship groups based on the new concept of time – variant fuzzy logical relationship group. Finally, Calculate the forecasted output value by the improved defuzzification technique in the stage of defuzzification. To evaluate performance of the proposed model, two numerical data sets are utilized to illustrate the proposed method and compare the forecasting accuracy with existing methods. The results show that the proposed model gets a higher average forecasting accuracy rate to forecast the Taiwan futures exchange (TAIFEX) and enrollments of the University of Alabama than the existing methods based on the first – order and high-order fuzzy time series.

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 A TIME SERIES FORECASTING MODEL BASED ON LINGUISTIC FORECASTING RULES

2021 ◽  
Vol 37 (1) ◽  
pp. 23-42
Author(s):  
Pham Đinh Phong
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Logical Relationship ◽  
Forecasting Models ◽  
Model Based ◽  
Time Invariant ◽  
Fuzzy Logical

The fuzzy time series (FTS) forecasting models have been being studied intensively over the past few years. Most of the researches focus on improving the effectiveness of the FTS forecasting models using time-invariant fuzzy logical relationship groups proposed by Chen et al. In contrast to Chen’s model, a fuzzy set can be repeated in the right-hand side of the fuzzy logical relationship groups of Yu’s model. N. C. Dieu enhanced Yu’s forecasting model by using the time-variant fuzzy logical relationship groups instead of the time-invariant ones. The forecasting models mentioned above partition the historical data into subintervals and assign the fuzzy sets to them by the human expert’s experience. N. D. Hieu et al. proposed a linguistic time series by utilizing the hedge algebras quantification to converse the numerical time series data to the linguistic time series. Similar to the FTS forecasting model, the obtained linguistic time series can define the linguistic, logical relationships which are used to establish the linguistic, logical relationship groups and form a linguistic forecasting model. In this paper, we propose a linguistic time series forecasting model based on the linguistic forecasting rules induced from the linguistic, logical relationships instead of the linguistic, logical relationship groups proposed by N. D. Hieu. The experimental studies using the historical data of the enrollments of University of Alabama observed from 1971 to 1992 and the daily average temperature data observed from June 1996 to September 1996 in Taipei show the outperformance of the proposed forecasting models over the counterpart ones.

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