On the Method of Identification of Atypical Observations in Time Series

Oesterreich Maciej ()
Additional contact information
Oesterreich Maciej: West Pomeranian University of Technology, Szczecin, Poland, Department of Applied Mathematics in Economics, Faculty of Economics

Econometrics. Advances in Applied Data Analysis, 2020, vol. 24, issue 2, 1-16

Abstract: The paper presents a method of detecting atypical observations in time series with or without seasonal fluctuations. Unlike classical methods of identifying outliers and influential observations, its essence consists in examining the impact of individual observations both on the fitted values of the model and the forecasts. The exemplification of theoretical considerations is the empirical example of modelling and forecasting daily sales of liquid fuels at X gas station in the period 2012-2014. As a predictor, a classic time series model was used, in which 7-day and 12-month cycle seasonality was described using dummy variables. The data for the period from 01.01.2012 to 30.06.2014 were for the estimation period and the second half of 2014 which was the period of empirical verification of forecasts. The obtained results were compared with other classical methods used to identify influential observations and outliers, i.e. standardized residuals, Cook distances and DFFIT. The calculations were carried out in the R environment and the Statistica package.

Keywords: forecasts; identification; multiple regression; time series; outliers (search for similar items in EconPapers)
JEL-codes: C32 C53 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.15611/eada.2020.2.01 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:eaiada:v:24:y:2020:i:2:p:1-16:n:1

DOI: 10.15611/eada.2020.2.01

Access Statistics for this article

Econometrics. Advances in Applied Data Analysis is currently edited by Józef Dziechciarz

More articles in Econometrics. Advances in Applied Data Analysis from Sciendo
Bibliographic data for series maintained by Peter Golla ().