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 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.

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

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.

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

Handling Forecasting Problems Based on Combining High-Order Time-Variant Fuzzy Relationship Groups and Particle Swam Optimization Technique

2018 ◽  
Vol 17 (02) ◽  
pp. 1850009
Author(s):  
Nghiem Van Tinh ◽  
Nguyen Cong Dieu
Keyword(s):  
Optimization Technique ◽  
Pso Algorithm ◽  
High Order ◽  
Fuzzy Time Series ◽  
Effective Length ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Proposed Model ◽  
History Of ◽  
High Order Time

Numerous fuzzy time series (FTS) forecasting methods have been proposed in scientific literature and has achieved growing attention in practice. Most of them are based on modeling fuzzy relationships(FRs) of the past data. In this paper, a new hybrid forecasting model based on the concept of time-variant FR group (TV-FRG), particle swarm optimization technique (PSO) and refinement technique in the defuzzization stage is presented to forecast the enrolments of the University and the stock market. PSO technique is utilized to adjust for obtaining the effective length of each interval in the universe of discourse for the forecasting model. Most of the existing forecasting models simply ignore the repeated FRs without any proper justification or accept the number of recurrence of the FRs without considering the appearance history of these FRs in the grouping FRs process. Therefore, the appearance history of the fuzzy sets on the right-hand side of the FRs is considered to establish the FR groups, called the TV-FRGs. Furthermore, the high-order TV-FRGs are used in order to obtain more accurate forecasting results in the defuzzication stage. To calculate these high-order TV-FRGs values, a refined defuzzication technique is developed, and incorporated in the proposed model. To verify the effectiveness of the proposed model, two numerical simulations are examined with the case of University enrollments and Taiwan futures exchange (TAIFEX). The experimental results show that the proposed model achieves good forecasting results compared to other existing forecasting models based on the high-order FTS. These promising results bring a significant meaning for the future work on the development of FTS and PSO algorithm in real-world forecasting applications.

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