Szczegóły publikacji
Opis bibliograficzny
Detection of periodic components from seasonal time series with moving trend method and low pass filtering / Jan T. DUDA, Tomasz PEŁECH-PILICHOWSKI // W: Advanced solutions in diagnostics and fault tolerant control / eds. Jan M. Kościelny, Michał Syfert, Anna Sztyber. — Cham : Springer, cop. 2018. — (Advances in Intelligent Systems and Computing ; ISSN 2194-5357 ; vol. 635). — Zawiera materiały z: DPS'2017 : 13th international conference on Diagnostics of Processes and Systems : Sandomierz, Poland, September 10–13, 2017. — ISBN: 978-3-319-64473-8; e-ISBN: 978-3-319-64474-5. — S. 192–202. — Bibliogr. s. 202, Abstr.
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 110909 |
|---|---|
| Data dodania do BaDAP | 2018-01-12 |
| DOI | 10.1007/978-3-319-64474-5_16 |
| Rok publikacji | 2018 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | 13th international conference on Diagnostics of Processes and Systems |
| Czasopismo/seria | Advances in Intelligent Systems and Computing |
Abstract
The paper presents the concept of time series decomposition by splitting into components with linear filtering methods. The modified moving trend algorithm (MTF) allows for more precise specification of desired trend properties and periodic component extraction from seasonal time series. In the paper the time and frequency properties of classical and modified FIR filters are presented and confronted with 4th order Butterworth filter. Three examples of empirical, seasonal time series are treated with the analyzed filters. Advantages and drawbacks of the proposed filters concerning the cyclic component extraction efficiency are discussed on the base of the processing results shown in time and frequency domain. Recommendations for the appropriate moving-trend bases filter selection suitable for processed time series properties are presented.