A comprehensive high order Type 2 fuzzy time series forecasting model

2016 ◽  
Author(s):  
Encheng Zhang ◽  
Degang Wang ◽  
Hongxing Li
Keyword(s):  
Time Series ◽  
Order Type ◽  
Time Series Forecasting ◽  
High Order ◽  
Fuzzy Time Series ◽  
Forecasting Model

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.

Multi-factor high-order intuitionistic fuzzy time series forecasting model

2016 ◽  
Vol 27 (5) ◽  
pp. 1054-1062 ◽  
Author(s):  
Ya'nan Wang ◽  
◽  
Yingjie Lei ◽  
Yang Lei ◽  
Xiaoshi Fan
Keyword(s):  
Time Series ◽  
Time Series Forecasting ◽  
High Order ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Intuitionistic Fuzzy

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.

A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model

2009 ◽  
Vol 36 (4) ◽  
pp. 7424-7434 ◽  
Author(s):  
Erol Egrioglu ◽  
Cagdas Hakan Aladag ◽  
Ufuk Yolcu ◽  
Murat A. Basaran ◽  
Vedide R. Uslu
Keyword(s):  
Time Series ◽  
Hybrid Approach ◽  
Time Series Forecasting ◽  
High Order ◽  
Fuzzy Time Series ◽  
Forecasting Model

scholarly journals A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 455 ◽  
Author(s):  
Hongjun Guan ◽  
Zongli Dai ◽  
Shuang Guan ◽  
Aiwu Zhao
Keyword(s):  
Time Series ◽  
Information Entropy ◽  
Stock Exchange ◽  
Time Series Forecasting ◽  
High Order ◽  
Stock Index ◽  
Neutrosophic Set ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Proposed Model

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

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