Title :
The interval autoregressive time series model
Author :
Wang, Xun ; Li, Shoumei
Author_Institution :
Dept. of Appl. Math., Beijing Univ. of Technol., Beijing, China
Abstract :
This paper mainly suggests a new type of interval time series: interval autoregressive (IAR) model. Firstly we state why we should introduce the interval time series models. Then we give necessary definitions about random intervals and interval time series. Thirdly, we introduce some methods of efficiency evaluation for forecasting of interval time series. And then we discuss parameter estimation and forecasting in IAR model, in which the methods of parameter estimation are based on the evaluation forecasting for interval data. Furthermore, we give the simulation results and apply it to real data from Shanghai Stock Index, which is to illustrate our modeling methodology. This model makes it possible for decision makers to forecast the best and worst possible situations based on interval-valued observations.
Keywords :
autoregressive processes; decision making; economic forecasting; parameter estimation; random processes; stock markets; time series; IAR model forecasting; Shanghai stock index; decision makers; efficiency evaluation; evaluation forecasting; interval autoregressive model; interval autoregressive time series model; interval data; interval time series forecasting; interval time series models; interval-valued observations; modeling methodology; parameter estimation; random intervals; Data models; Forecasting; Parameter estimation; Predictive models; Random variables; Time series analysis; White noise; IAR model; Interval time series; forecasting of stock price; parameter estimation;
Conference_Titel :
Fuzzy Systems (FUZZ), 2011 IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-7315-1
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2011.6007470
