Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)

03/01/2018

∙

The aim this study is discussed on the detection and correction of data containing the additive outlier (AO) on the model ARIMA (p, d, q). The process of detection and correction of data using an iterative procedure popularized by Box, Jenkins, and Reinsel (1994). By using this method we obtained an ARIMA models were fit to the data containing AO, this model is added to the original model of ARIMA coefficients obtained from the iteration process using regression methods. This shows that there is an improvement of forecasting error rate data.

READ FULL TEXT

Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)

Multiple testing for outlier detection in functional data

Outlier Detection and Spatial Analysis Algorithms

Multivariate Functional Outlier Detection using the FastMUOD Indices

Efficient variational Bayesian neural network ensembles for outlier detection

Statistical Depth based Normalization and Outlier Detection of Gene Expression Data

Efficient remedies for outlier detection with variational autoencoders

Outlier Guided Optimization of Abdominal Segmentation

Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)

Related Research

Multiple testing for outlier detection in functional data

Outlier Detection and Spatial Analysis Algorithms

Multivariate Functional Outlier Detection using the FastMUOD Indices

Efficient variational Bayesian neural network ensembles for outlier detection

Statistical Depth based Normalization and Outlier Detection of Gene Expression Data

Efficient remedies for outlier detection with variational autoencoders

Outlier Guided Optimization of Abdominal Segmentation