On the Method of Identification of Atypical Observations in Time Series

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...

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Main Author: Maciej Oesterreich
Format: Article
Language:English
Published: Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu 2020-01-01
Series:Ekonometria
Online Access:https://journals.ue.wroc.pl/eada/article/view/922
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author Maciej Oesterreich
author_facet Maciej Oesterreich
author_sort Maciej Oesterreich
collection DOAJ
description 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.(original abstract)
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institution DOAJ
issn 2449-9994
language English
publishDate 2020-01-01
publisher Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
record_format Article
series Ekonometria
spelling doaj-art-b6948c6e68554e5fb4a75ce80368a1b32025-08-20T03:08:21ZengWydawnictwo Uniwersytetu Ekonomicznego we WrocławiuEkonometria2449-99942020-01-01nr 2923On the Method of Identification of Atypical Observations in Time SeriesMaciej Oesterreich0West Pomeranian University of Technology SzczecinThe 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.(original abstract)https://journals.ue.wroc.pl/eada/article/view/922
spellingShingle Maciej Oesterreich
On the Method of Identification of Atypical Observations in Time Series
Ekonometria
title On the Method of Identification of Atypical Observations in Time Series
title_full On the Method of Identification of Atypical Observations in Time Series
title_fullStr On the Method of Identification of Atypical Observations in Time Series
title_full_unstemmed On the Method of Identification of Atypical Observations in Time Series
title_short On the Method of Identification of Atypical Observations in Time Series
title_sort on the method of identification of atypical observations in time series
url https://journals.ue.wroc.pl/eada/article/view/922
work_keys_str_mv AT maciejoesterreich onthemethodofidentificationofatypicalobservationsintimeseries