Visualizing the relationship between two time series by hierarchical smoothing models?

Jan Beran, Guerino Mazzola

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Let x(ti), y(ti) be two time series such that y(ti) = μ(ti, x) + εi, where μ is a smooth function and εi is a zero mean stationary process. Which model may be assumed for μ depends on the subject specific context. This article was motivated by questions raised in the context of musical performance theory. The general problem is to understand the relationship between the symbolic structure of a music score and its performance. Musical structure typically consists of a hierarchy of global and local structures. This motivates the definition of hierarchical smoothing models (or HISMOOTH models) that are characterized by a hierarchy of bandwidths b 1 > b 2 > … > bM and a vector of coefficients β ∈ RM. The expected value μ(ti x) = E[y(ti)‖x] is equal to a weighted sum of smoothed versions of x. The “errors” εi are modeled by a Gaussian process that may exhibit long memory. More generally, we may observe a collection of time series yr (r = 1, …, N) that are related to a common time series x by yr(ti) = μ r(ti, x) + εr, i where ε r are independent error processes. For repeated time series, HISMOOTH models lead to a visual and formal classification into clusters that can be interpreted in terms of the relationship to x. An analysis of tempo curves from 28 performances of Schumann's “Träumerei” op. 15/7 illustrates the method. In particular, similarities and differences of “melodic styles” can be identified.

Original languageEnglish (US)
Pages (from-to)213-238
Number of pages26
JournalJournal of Computational and Graphical Statistics
Volume8
Issue number2
DOIs
StatePublished - Jun 1999
Externally publishedYes

Keywords

  • Fractional autoregressive model
  • Hierarchical smoothing
  • Kernel smoothing
  • Long-range dependence
  • Musical performance
  • Musical structure
  • Time series

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