The decomposition issue of a time series in the forecasting process
Authors:
- Dariusz Grzesica
Abstract
Decomposition of time series is the estimate and extraction of deterministic part of the series – trend, cyclical and seasonal fluctuations in the hope that the rest of the data, that is, theoretically, a random variable will be stationary random process. During the process of predicting the time series elements affects significantly on the determination of the future values, which are characterized by a low forecast error. Therefore, the purpose of this article is to identify the elements of the time series decomposition and to determine the extent to which they affect the forecasting process. Problems that often appear when you run the forecast and methods of building models and forecasts based on time series will be presented. Observations will be described on the basis of nonparametric time series modeling.
- Record ID
- CUT77ff7bd263a94b0782a1278a80b2295a
- Publication categories
- ; ;
- Author
- Journal series
- International Conference Knowledge-Based Organization : conference proceedings, ISSN 2451-3113
- Issue year
- 2017
- Vol
- 23
- No
- 3
- Pages
- 43-47
- Other elements of collation
- wykr.; Bibliografia (na s.) - 47; Bibliografia (liczba pozycji) - 12; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 23, No 3
- Conference
- 23rd International Conference the Knowledge-Based Organization (KBO), 2017, 15-06-2017 - 17-06-2017, Sibiu, Rumunia
- Keywords in English
- time series decomposition, seasonal fluctuations, cyclicity
- DOI
- DOI:10.1515/kbo-2017-0154 Opening in a new tab
- URL
- https://doi.org/10.1515/kbo-2017-0154 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 1
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT77ff7bd263a94b0782a1278a80b2295a/
- URN
urn:pkr-prod:CUT77ff7bd263a94b0782a1278a80b2295a
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.