A Method for Detection of Outliers in Time Series Data

Abstract

An outlier is a data value that is an unusually small or large, or that deviates from the pattern of the rest of the data. Outliers are usually removed from the data set before fitting a forecasting model, or not removed but the forecasting model adjusted in presence of outliers. There are four types of OUTLIERS are as follows: Additive outlier (AO), Innovational outlier (IO), Level shift (LS) and Temporary change (TC). There is more than one method for the detection of outlier; the study considers the detection of outlier in two cases: first, at the time when the parameters are known. Second, when the parameters are unknown. There are several reasons for outlier detection and adjustment in time series analysis and forecasting which are mentioned in this study. The study has used the volume of water inflow in the reservoir of Dokan dam in Sulaymaniah city as a time series for the purpose of the study. The study came to conclude that throughout the research, the following conclusions: first, every time increasing the critical value, the value of residual standard error (with outlier adjustment) increased. Second, every time increasing the critical value, the number of outlier values decreased. Third, in the case of the presence of outliers the forecasts with adjustment of outliers better than the forecasts without adjusting outliers.