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
Forecasting enrollments based on fuzzy time series
Fuzzy Sets and Systems
A bivariate fuzzy time series model to forecast the TAIEX
Expert Systems with Applications: An International Journal
A SIMPLE TIME VARIANT METHOD FOR FUZZY TIME SERIES FORECASTING
Cybernetics and Systems
Expert Systems with Applications: An International Journal
Improved time-variant fuzzy time series forecast
Fuzzy Optimization and Decision Making
Fuzzy relation analysis in fuzzy time series model
Computers & Mathematics with Applications
ARFNNs with SVR for prediction of chaotic time series with outliers
Expert Systems with Applications: An International Journal
Ratio-based lengths of intervals to improve fuzzy time series forecasting
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
High-order fuzzy-neuro expert system for time series forecasting
Knowledge-Based Systems
| Hi-index | 12.06 |
The study demonstrates the superiority of fuzzy based methods for non-stationary, non-linear time series. Study is based on unequal length fuzzy sets and uses IF-THEN based fuzzy rules to capture the trend prevailing in the series. The proposed model not only predicts the value but can also identify the transition points where the series may change its shape and is ready to include subject expert's opinion to forecast. The series is tested on three different types of data: enrolment for Alabama university, sales volume of a chemical company and Gross domestic capital of India: the growth curve. The model is tested on both kind of series: with and without outliers. The proposed model provides an improved prediction with lesser MAPE (mean average percentage error) for all the series tested.