Bayesian Model for Time Series with Trend, Autoregression and Outliers

被引：0

作者：

Tongkhow, Pitsanu ^{[1
]}

Kantanantha, Nantachai ^{[1
]}

机构：

[1] Kasetsart Univ, Dept Ind Engn, Fac Engn, Bangkok, Thailand

来源：

2012 TENTH INTERNATIONAL CONFERENCE ON ICT AND KNOWLEDGE ENGINEERING | 2012年

关键词：

Bayesian method; time series; trend; autoregression; outliers; cumulative Weibull distribution;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We propose the Bayesian forecasting model that can detect trend, autoregression, and outliers in the time series data. We use cumulative Weibull distribution function for trend, binary selection for outliers, and autoregression for related time series data. Gibbs sampling algorithm which is one of MCMC methods is used for parameter estimation. The proposed models are applied to the vegetable price time series data in Thailand. According to the RMSE, MAPE, and MAE criteria for the model comparison, the proposed model provides the best results compared to the exponential smoothing and SARIMA models.

引用

页码：90 / 94

页数：5