Detecting Aberrant Values and Their Influence on the Time Series Forecast

This article addresses the influence of outliers on building time series models. Two methods for detecting aberrant values are discussed, and models are built for the studied data series in the outliers' presence and absence. Data used consisted in the annual precipitation series recorded at Sulina (Romania). The models have been further employed for generating precipitation fields. This process shows a good concordance of the historical data and the forecast in terms of mean, minimum, maximum values, and minimum recorded precipitation in two, five, seven, and ten successive years. The results show that the field generated after the outliers' removal is better in terms of ten statistical indicators.