Related Experiment Video
Updated: Jul 9, 2026
10:46
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
A multivariate heuristic model for fuzzy time-series forecasting
Kun-Huang Huarng1, Tiffany Hui-Kuang Yu, Yu Wei Hsu
1Department of International Trade, Feng Chia University, Taichung 40724, Taiwan, ROC. khhuarng@fcu.edu.tw
Summary
This study introduces a new multivariate heuristic function to enhance fuzzy time-series models. The method improves forecasting accuracy for nonlinear data while simplifying complex matrix computations.
Area of Science:
- Artificial Intelligence
- Data Science
- Time Series Analysis
Background:
- Fuzzy time-series models excel at handling nonlinear data without strict assumptions.
- These models often outperform traditional forecasting methods.
- However, complex matrix computations can be a limitation.
Purpose of the Study:
- To propose a novel multivariate heuristic function for fuzzy time-series models.
- To integrate this function with univariate models to create effective multivariate models.
- To enhance forecasting accuracy and computational efficiency.
Main Methods:
- Development of a multivariate heuristic function.
- Integration of the heuristic function with existing univariate fuzzy time-series models.
- Application to handle multiple variables in forecasting.
Main Results:
- The integrated model successfully handles multiple variables.
- Forecasting results are improved compared to conventional methods.
- Complicated matrix computations are avoided, simplifying the process.
Conclusions:
- The proposed multivariate heuristic function offers a computationally efficient approach to fuzzy time-series forecasting.
- This method enhances the capability of fuzzy time-series models in handling complex, nonlinear, and multivariate data.
- It provides a flexible framework for extending various univariate models.
Related Concept Videos
01:03Prediction Intervals
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
The...
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
The...
01:25Multiple Regression
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
01:22Multi-input and Multi-variable systems
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
In the absence of...
01:14Multicompartment Models: Overview
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
01:19Survival Tree
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a survival tree begins...
Building a Survival Tree
Constructing a survival tree begins...
01:23Quadratic Models
Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...