Fuzzy time series and its models
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part I
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part II
Fuzzy Sets and Systems
A comparison of fuzzy forecasting and Markov modeling
Fuzzy Sets and Systems
Data mining: concepts and techniques
Data mining: concepts and techniques
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Evolving Connectionist Systems: The Knowledge Engineering Approach
Evolving Connectionist Systems: The Knowledge Engineering Approach
Expert Systems with Applications: An International Journal
Deterministic fuzzy time series model for forecasting enrollments
Computers & Mathematics with Applications
Fuzzy time-series based on adaptive expectation model for TAIEX forecasting
Expert Systems with Applications: An International Journal
A bivariate fuzzy time series model to forecast the TAIEX
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A computational method of forecasting based on high-order fuzzy time series
Expert Systems with Applications: An International Journal
A neural network-based fuzzy time series model to improve forecasting
Expert Systems with Applications: An International Journal
A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting
Information Sciences: an International Journal
Deterministic vector long-term forecasting for fuzzy time series
Fuzzy Sets and Systems
Evolving Intelligent Systems: Methodology and Applications
Evolving Intelligent Systems: Methodology and Applications
Knowledge Discovery from Data Streams
Knowledge Discovery from Data Streams
A hybrid ANFIS model based on AR and volatility for TAIEX forecasting
Applied Soft Computing
Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications
Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications
Preface: Adaptive incremental learning in neural networks
Neurocomputing
Forecasting stock price based on fuzzy time-series with entropy-based discretization partitioning
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Learning in Non-Stationary Environments: Methods and Applications
Learning in Non-Stationary Environments: Methods and Applications
Ratio-based lengths of intervals to improve fuzzy time series forecasting
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Editorial: Editorial of the special issue: Online fuzzy machine learning and data mining
Information Sciences: an International Journal
| Hi-index | 0.00 |
Fuzzy time series analysis has been used successfully for forecasting in various domains including stock performance, academic enrollment, temperature, and traffic patterns. Research in this field has concentrated primarily on two issues: the reasonable partition of discourse, and defuzzification methods for discrete datasets. Both issues have a huge impact on the prediction performance of forecasting models. This paper integrates the entropy discretization technique with a Fast Fourier Transform (FFT) algorithm to develop a novel fuzzy time series forecasting model to resolve these issues. The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Dow-Jones Industrial Average (DJIA) financial datasets were used to evaluate the model's performance. The results demonstrate that the presented model is a major improvement over previous fuzzy time series models produced by Chen (1996), Yu (2005), Chang et al. (2011), and Hsieh et al. (2011), and five other conventional time series models. The proposed model is implemented using the bootstrapping method, after which it incrementally updates its prediction capability. Results show that the proposed model's incremental learning mechanism allows it to effectively handle large online financial datasets.