Temporal clustering of time series via threshold autoregressive models: application to commodity prices

Download

index.pdf

Date

2018-01-01

Author

Aslan, Sipan
Yozgatlıgil, Ceylan
İyigün, Cem

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

217
views

108
downloads

The primary aim in this study is grouping time series according to the similarity between their data generating mechanisms (DGMs) rather than comparing pattern similarities in the time series trajectories. The approximation to the DGM of each series is accomplished by fitting the linear autoregressive and the non-linear threshold autoregressive models, and outputs of the estimates are used for feature extraction. Threshold autoregressive models are recognized for their ability to represent nonlinear features in time series, such as abrupt changes, time-irreversibility and regime-shifting behavior. The proposed clustering approach is mainly based on feature vectors derived from above-mentioned models estimates. Through the use of the proposed approach, one can determine and monitor the set of co-moving time series variables across the time. The efficiency of the proposed approach is demonstrated through a simulation study and the results are compared with other proposed time series clustering methods. An illustration of the proposed clustering approach is given by application to several commodity prices. It is expected that the process of determining the commodity groups that are time-dependent will advance the current knowledge about temporal behavior and the dynamics of co-moving and coherent prices, and can serve as a basis for multivariate time series analyses. Furthermore, generating a time varying commodity prices index and sub-indexes can become possible. Findings suggested that clusters of the prices series have been affected with the global financial crisis in 2008 and the data generating mechanisms of prices and so the clusters of prices might not be the same across the entire time-period of the analysis.

Subject Keywords

Commodity prices, Spectral clustering, Time series clustering

URI

https://hdl.handle.net/11511/46571

Journal

ANNALS OF OPERATIONS RESEARCH

DOI

https://doi.org/10.1007/s10479-017-2659-0

Collections

Department of Statistics, Article

Suggestions

OpenMETU
Core

TEMPORAL CLUSTERING OF MULTIVARIATE TIME SERIES Aslan, Sipan; Yozgatlıgil, Ceylan; İyigün, Cem; Department of Statistics (2022-2-07) Clustering of real-valued time series is a prevalent problem that frequently emerges in various fields and applications. While clustering of univariate time series is very much examined, clustering of multivariate time series has not been extensively addressed. This dissertation considers the clustering of real-valued multivariate time series data. When the data analyzed in the clustering task are time series, the time dependencies of the time series and the clusters to be formed should be considered togeth...
Consensus clustering of time series data Yetere Kurşun, Ayça; Batmaz, İnci; İyigün, Cem; Department of Scientific Computing (2014) In this study, we aim to develop a methodology that merges Dynamic Time Warping (DTW) and consensus clustering in a single algorithm. Mostly used time series distance measures require data to be of the same length and measure the distance between time series data mostly depends on the similarity of each coinciding data pair in time. DTW is a relatively new measure used to compare two time dependent sequences which may be out of phase or may not have the same lengths or frequencies. DTW aligns two time serie...
Kernel probabilistic distance clustering algorithms Özkan, Dilay; İyigün, Cem; Department of Industrial Engineering (2022-7) Clustering is an unsupervised learning method that groups data considering the similarities between objects (data points). Probabilistic Distance Clustering (PDC) is a soft clustering approach based on some principles. Instead of directly assigning an object to a cluster, it assigns them to clusters with a membership probability. PDC is a simple yet effective clustering algorithm that performs well on spherical-shaped and linearly separable data sets. Traditional clustering algorithms fail when the data ...
Phase resolved spectral analysis of selected intermediate polars and the effect of warm absorbers in cataclysmic variables Pekön, Yakup; Balman, Şölen; Department of Physics (2014) In this study, XMM-Newton data of selected cataclysmic variables are examined. One of the sources is an Intermediate Polar (IP) EX Hya where the orbital and spin phase resolved spectra are analyzed in depth. A composite model is successfully fit to the spectra in order to explain the X-ray emission from the source. Changes in the emission, absorption and accretion behaviour of the system over the orbital and spin period of the system, as well as the changes in the system over two different epoch are determi...
PROGRESSIVE CLUSTERING OF MANIFOLD-MODELED DATA BASED ON TANGENT SPACE VARIATIONS Gokdogan, Gokhan; Vural, Elif (2017-09-28) An important research topic of the recent years has been to understand and analyze manifold-modeled data for clustering and classification applications. Most clustering methods developed for data of non-linear and low-dimensional structure are based on local linearity assumptions. However, clustering algorithms based on locally linear representations can tolerate difficult sampling conditions only to some extent, and may fail for scarcely sampled data manifolds or at high-curvature regions. In this paper, w...

Citation Formats

S. Aslan, C. Yozgatlıgil, and C. İyigün, “Temporal clustering of time series via threshold autoregressive models: application to commodity prices,” ANNALS OF OPERATIONS RESEARCH, pp. 51–77, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/46571.