A vector forecasting model for fuzzy time series

Li, Sheng-Tun; Kuo, Shu-Ching; Cheng, Yi-Chung; Chen, Chih-Chuan

doi:10.1016/j.asoc.2010.12.015

Cited by 34 publications

(15 citation statements)

References 32 publications

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“…Later, Li et al [13] proposed a deterministic vector model for long-term fuzzy time series forecasting. They then proposed another deterministic vector forecasting model, adopting the techniques of sliding windows and a fuzzy clustering method, for one-factor time-invariant fuzzy time series problems [14].…”

Section: Introductionmentioning

confidence: 99%

“…The second dataset is the daily closing price of Google stock collected during the period from 1 January 2009 to 6 October 2010. We implemented four measures, including root mean square error (RMSE), trend accuracy in direction (TAD) [13,14], percent mean absolute deviation (PMAD), and mean absolute percentage error (MAPE), to evaluate the performance of the proposed method. The results show that the proposed model achieves a significant improvement in forecasting accuracy compared to the original one.…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary Fuzzy Relational Modeling for Fuzzy Time Series Forecasting

Kuo

Chen

2015

Int. J. Fuzzy Syst.

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The use of fuzzy time series has attracted considerable attention in studies that aim to make forecasts using uncertain information. However, most of the related studies do not use a learning mechanism to extract valuable information from historical data. In this study, we propose an evolutionary fuzzy forecasting model, in which a learning technique for a fuzzy relation matrix is designed to fit the historical data. Taking into consideration the causal relationships among the linguistic terms that are missing in many existing fuzzy time series forecasting models, this method can naturally smooth the defuzzification process, thus obtaining better results than many other fuzzy time series forecasting models, which tend to produce stepwise outcomes. The experimental results with two real datasets and four indicators show that the proposed model achieves a significant improvement in forecasting accuracy compared to earlier models.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evolutionary Fuzzy Relational Modeling for Fuzzy Time Series Forecasting

Kuo

Chen

2015

Int. J. Fuzzy Syst.

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“…The objective of the study was to examine the effectiveness of forecasting for remanufactured products by time series analysis. Li et al [12] used vector forecasting model for fuzzy time series which were capable of dealing with ambiguity. The contribution of the work was to improve forecasting capability through the expansion of the vector forecasting model.…”

Section: Journal Of Industrial Engineeringmentioning

confidence: 99%

A Study of Time Series Model for Predicting Jute Yarn Demand: Case Study

Karmaker

Halder

Sarker

2017

Journal of Industrial Engineering

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In today's competitive environment, predicting sales for upcoming periods at right quantity is very crucial for ensuring product availability as well as improving customer satisfaction. This paper develops a model to identify the most appropriate method for prediction based on the least values of forecasting errors. Necessary sales data of jute yarn were collected from a jute product manufacturer industry in Bangladesh, namely, Akij Jute Mills, Akij Group Ltd., in Noapara, Jessore. Time series plot of demand data indicates that demand fluctuates over the period of time. In this paper, eight different forecasting techniques including simple moving average, single exponential smoothing, trend analysis, Winters method, and Holt's method were performed by statistical technique using Minitab 17 software. Performance of all methods was evaluated on the basis of forecasting accuracy and the analysis shows that Winters additive model gives the best performance in terms of lowest error determinants. This work can be a guide for Bangladeshi manufacturers as well as other researchers to identify the most suitable forecasting technique for their industry.

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“…Constructing an HMM model with . First, the initial state vector  is set to be a n  1 matrix, defined as: (14) where is the number of the data whose initial states are and . Next, the state transition matrix is a n n  matrix defined as below: (15) with and , where denotes that the number of data whose hidden states is at time and at time .…”

Section: Model Developmentmentioning

confidence: 99%

An Enhanced HMM-Based for Fuzzy Time Series Forecasting Model

Cheng¹,

Chen²,

Chen³

et al. 2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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The fast and accurate forecasting method can help makers to make appropriate strategy. Zadeh was given the definition of a fuzzy set in 1965. Song and Chissom proposed the definition and the forecasting framework of fuzzy time series in 1993. Sullivan and Woodall first proposed the forecasting method to handle one factor with probability Markov model in 1994. Li and Cheng proposed a stochastic hidden Markov model which considers two factors in 2010. However, an event can be affected by many factors. In this paper, we present a multi-factor HMM-based forecasting, and utilize more factors to get better forecasting accuracy rate.

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A vector forecasting model for fuzzy time series

Cited by 34 publications

References 32 publications

Evolutionary Fuzzy Relational Modeling for Fuzzy Time Series Forecasting

Evolutionary Fuzzy Relational Modeling for Fuzzy Time Series Forecasting

A Study of Time Series Model for Predicting Jute Yarn Demand: Case Study

An Enhanced HMM-Based for Fuzzy Time Series Forecasting Model

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