Time series clustering and classification by the autoregressive metric

Corduas, Marcella; Piccolo, Domenico

doi:10.1016/j.csda.2007.06.001

Cited by 171 publications

(86 citation statements)

References 42 publications

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“…Piccolo (1990) proposed a metrics measuring the distance between two ARMA models, based on the comparison of the coefficients of their AR(∞) representation. This tool has had a large success in several fields; a review of its properties and applications with several references can be found in Piccolo (2007) and Corduas and Piccolo (2008).…”

Section: Unconditional Minimum and Time-varying Volatilitiesmentioning

confidence: 99%

“…In particular, he distinguishes three major categories of approaches to time series clustering: 1) raw-data-based approaches, in which the series compared are considered as normally sampled at the same interval; 2) features-based approaches, in which the series are compared using some selected features; 3) model-based methods, where the time series are considered similar when the models characterizing them are similar. The approach proposed in this work belongs to the third category; in particular it follows the tradition of AR processes to capture the similarity among time series, as in Piccolo (1990), Maharaj (1996Maharaj ( , 1999Maharaj ( , 2000, Xiong and Yeung (2002) (see Piccolo, 2007, and Corduas and Piccolo, 2008, for a review). Most of these studies are devoted to capturing the structure of the mean of the process hypothesized as generator of the data, whereas little attention was put on the variance.…”

Section: Introductionmentioning

confidence: 99%

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Clustering heteroskedastic time series by model-based procedures

Otranto¹

2008

Computational Statistics & Data Analysis

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Section: Unconditional Minimum and Time-varying Volatilitiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Clustering heteroskedastic time series by model-based procedures

Otranto¹

2008

Computational Statistics & Data Analysis

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“…Many metrics that measure the similarity between time series have been proposed in the last two decades. There are two different approaches for identifying the similarity, as reviewed by Corduas and Piccolo (2008).…”

Section: Introductionmentioning

confidence: 99%

Classification of Time-Series Data Based on Several Lag Windows

Kim¹,

Park

2010

Communications for Statistical Applications and Methods

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In the case of time-series analysis, it is often more convenient to rely on the frequency domain than the time domain. Spectral density is the core of the frequency-domain analysis that describes autocorrelation structures in a time-series process. Possible ways to estimate spectral density are to compute a periodogram or to average the periodogram over some frequencies with (un)equal weights. This can be an attractive tool to measure the similarity between time-series processes. We employ the metrics based on a smoothed periodogram proposed by Park and Kim (2008) for the classification of different classes of time-series processes. We consider several lag windows with unequal weights instead of a modified Daniel's window used in Park and Kim (2008). We evaluate the performance under various simulation scenarios. Simulation results reveal that the metrics used in this study split the time series into the preassigned clusters better than do the raw-periodogram based ones proposed by Caiado et al. (2006). Our metrics are applied to an economic time-series dataset.

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“…The problem of time series clustering has also been explored in the literature from different perspectives; see, e.g., [2,4,5,15,22,23,25].…”

Section: Introductionmentioning

confidence: 99%

A double clustering algorithm for financial time series based on extreme events

Luca

Zuccolotto

2016

Statistics &Amp; Risk Modeling

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This paper is concerned with a procedure for financial time series clustering, aimed at creating groups of time series characterized by similar behavior with regard to extreme events. The core of our proposal is a double clustering procedure: the former is based on the lower tail dependence of all the possible pairs of time series, the latter on the upper tail dependence. Tail dependence coefficients are estimated with copula functions. The final goal is to exploit the two clustering solutions in an algorithm designed to create a portfolio that maximizes the probability of joint positive extreme returns while minimizing the risk of joint negative extreme returns. In financial crisis scenarios, such a portfolio is expected to outperform portfolios generated by the traditional methods. We describe the results of a simulation study and, finally, we apply the procedure to a dataset composed of the 50 assets included in the EUROSTOXX index.

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Time series clustering and classification by the autoregressive metric

Cited by 171 publications

References 42 publications

Clustering heteroskedastic time series by model-based procedures

Clustering heteroskedastic time series by model-based procedures

Classification of Time-Series Data Based on Several Lag Windows

A double clustering algorithm for financial time series based on extreme events

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