A New Forecasting Model of Fuzzy Time Series

2014 ◽  
Vol 678 ◽  
pp. 59-63 ◽  
Author(s):  
Hong Xu Wang ◽  
Jian Chun Guo ◽  
Hao Feng ◽  
Hai Long Jin
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Historical Data ◽  
Time Series Forecasting ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
Forecasting Model

Classical time series model can efficiently handle many forecasting problems, but these models can not solves the forecasting problem in which values of the time series are represented by language values or fuzzy sets. Song and Chissom and many other scholars put forward many models, and these models can only forecast research about historical data. This paper presents a new fuzzy time series forecasting model which can predict the data of unknown years.

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.

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.

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.

A New Type 2 Fuzzy Time Series Forecasting Model Based on Three-Factors Fuzzy Logical Relationships

2019 ◽  
Vol 27 (02) ◽  
pp. 251-276 ◽  
Author(s):  
Abhishekh ◽  
Surendra Singh Gautam ◽  
Shiva Raj Singh
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Logical Relationship ◽  
New Type ◽  
Accuracy Rates ◽  
Fuzzy Logical

The study of fuzzy time series models have been extensively used to improve the accuracy rates in forecasting problems. In this paper, we present a new type 2 fuzzy time series forecasting model based on three-factors fuzzy logical relationship groups. The proposed method uses a new technique to define partitions the universe of discourse into different length of intervals for different factors. Also, the proposed method fuzzifies the historical data sets of the main factors, second factors and third factors to their maximum membership grades obtained by their corresponding triangular fuzzy sets and construct the fuzzy logical relationship groups which is based on the three-factors to enhance in the forecasting accuracy rates. This paper introduces a new defuzzification technique based on their frequency occurrences of fuzzy logical relationships in fuzzy logical relationship groups. The fitness of the propose method is verified in the forecasting of Bombay Stock Exchange (BSE) Sensex historical data and compare in terms of root mean square and average forecasting errors which indicates that the proposed method produce more accurate forecasted output over the existing models in fuzzy time series.

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.

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