Mathematical optimization for time series decomposition

Name: Mathematical optimization for time series decomposition
Author: Gözüyılmaz, Şeyma, Kundakcıoğlu, Ömer Erhun

İsim	Mathematical optimization for time series decomposition
Yazar	Gözüyılmaz, Şeyma, Kundakcıoğlu, Ömer Erhun
Basım Tarihi:	2021-09
Basım Yeri	- Springer
Konu	Time series, Seasonal trend decomposition, Mixed integer nonlinear programming
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0171-6468
Kayıt Numarası	da2b69af-9f5c-4748-9dc8-69bdd399e5a4
Lokasyon	Industrial Engineering
Tarih	2021-09
Örnek Metin	Decomposing time series into trend and seasonality components reveals insights used in forecasting and anomaly detection. This study proposes a mathematical optimization approach that addresses several data-related issues in time series decomposition. Our approach does not only handle longer and multiple seasons but also identifies outliers and trend shifts. Numerical experiments on real-world and synthetic problem sets present the effectiveness of the proposed approach.
DOI	10.1007/s00291-021-00637-w
Cilt	43

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

Mathematical optimization for time series decomposition

Yazar Gözüyılmaz, Şeyma, Kundakcıoğlu, Ömer Erhun

Basım Tarihi 2021-09

Basım Yeri - Springer

Konu Time series, Seasonal trend decomposition, Mixed integer nonlinear programming

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0171-6468

Kayıt Numarası da2b69af-9f5c-4748-9dc8-69bdd399e5a4

Lokasyon Industrial Engineering

Tarih 2021-09

Örnek Metin Decomposing time series into trend and seasonality components reveals insights used in forecasting and anomaly detection. This study proposes a mathematical optimization approach that addresses several data-related issues in time series decomposition. Our approach does not only handle longer and multiple seasons but also identifies outliers and trend shifts. Numerical experiments on real-world and synthetic problem sets present the effectiveness of the proposed approach.

DOI 10.1007/s00291-021-00637-w

Cilt 43