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Adaptive Non-Stationary Fuzzy Time Series Forecasting with Bayesian Networks [PDF]
SensorsDespite its interpretability and excellence in time series forecasting, the fuzzy time series forecasting model (FTSFM) faces significant challenges when handling non-stationary time series.
Bo Wang, Xiaodong Liu
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Multilayer Stock Forecasting Model Using Fuzzy Time Series [PDF]
The Scientific World Journal, 2014 After reviewing the vast body of literature on using FTS in stock market forecasting, certain deficiencies are distinguished in the hybridization of findings.
Hossein Javedani Sadaei +1 more
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Identifying Outliers in Fuzzy Time Series [PDF]
Journal of Modern Applied Statistical Methods, 2011 Time series analysis is often associated with the discovery of patterns and prediction of features. Forecasting accuracy can be improved by removing identified outliers in the data set using the Cook’s distance and Studentized residual test.
Kannan, K. Senthamarai, Suresh, S.
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Fuzzy Information Granules in Time Series Data [PDF]
, 2004 Often, it is desirable to represent a set of time series through typical shapes in order to detect common patterns. The algorithm presented here compares pieces of a different time series in order to find such similar shapes.
DAVID PATTERSON +5 more
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Seasonal Time Series Forecasting by F1‐Fuzzy Transform [PDF]
Sensors, 2019 We present a new seasonal forecasting method based on F1-transform (fuzzy transform of order 1) applied on weather datasets. The objective of this research is to improve the performances of the fuzzy transform-based prediction method applied to seasonal ...
DI MARTINO, Ferdinando, Sessa, Salvatore
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Cheng Fuzzy Time Series Model to Forecast the Price of Crude Oil in Malaysia
Journal of Computing Research and Innovation, 2022 Crude oil is one of the important commodities to Malaysia. As a producer and exporter of oil and gas, Malaysia has gained high Gross Revenue from this sector. Crude oil is the global commodity and highly demanded.
Jasmani Bidin +4 more
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Intuitionistic fuzzy time series functions approach for time series forecasting [PDF]
Granular Computing, 2020 AbstractFuzzy inference systems have been commonly used for time series forecasting in the literature. Adaptive network fuzzy inference system, fuzzy time series approaches and fuzzy regression functions approaches are popular among fuzzy inference systems.
Egrioglu, Erol, Bas, Eren, Yolcu, Ufuk
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A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data
Mathematics, 2023 In this paper, a nonlinear time series model is developed for the case when the underlying time series data are reported by LR fuzzy numbers. To this end, we present a three-stage nonparametric kernel-based estimation procedure for the center as well as ...
Gholamreza Hesamian +2 more
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Temperature prediction using fuzzy time series [PDF]
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2000 A drawback of traditional forecasting methods is that they can not deal with forecasting problems in which the historical data are represented by linguistic values. Using fuzzy time series to deal with forecasting problems can overcome this drawback. In this paper, we propose a new fuzzy time series model called the two-factors time-variant fuzzy time ...
Chen, S. M., Hwang, J. R.
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Average-Based Fuzzy Time Series Markov Chain Based on Frequency Density Partitioning
Journal of Applied Mathematics, 2023 Fuzzy time series (FTS) is one of the forecasting methods that has been developed until now. The fuzzy time series is a forecasting method that uses the concept of fuzzy logic, which Song and Chissom first introduced.
Susilo Hariyanto +3 more
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