Previous Chapter Chapter 38: Fuzzy Time Series Models Next Chapter

Kun-Huang Huarng, Luiz Moutinho & Tiffany Hui-Kuang Yu

In: The SAGE Dictionary of Quantitative Management Research

Chapter 38: Fuzzy Time Series Models

  • Citations
  • Add to My List
  • Text Size

Fuzzy Time Series Models
Fuzzy time series models
Introduction

Conventional time series models have long been studied and applied to forecasts. Fuzzy time series models, as a counterpart of the conventional time series models, were proposed by Song and Chissom (1993). Following that study, many relevant studies have been proposed to forecast various problems. Interestingly, many of the forecasting results from these studies have been shown to outperform their conventional counterparts.

Key Features

Fuzzy time series models can be univariate, bivariate, or multivariate models, which means that we can use one variable, two variables, or multiple variables to forecast. In addition, the models can be autoregressive of order 1 (AR(1)), which means we can use the observation at t − 1 to forecast the one at t. In general, the ...

Looks like you do not have access to this content.

Login

Don’t know how to login?

Click here for free trial login.

Back to Top

Copy and paste the following HTML into your website