Recurrence-based reconstruction of dynamic pricing attractors

Lu, S; Oberst, S

Recurrence-based reconstruction of dynamic pricing attractors

Lu, S Oberst, S

Permalink

Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Nonlinear Dynamics, 2023, 111, (16), pp. 15263-15278
Issue Date:: 2023-08

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (4.78 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Lu, S
dc.contributor.author	Oberst, S https://orcid.org/0000-0002-1388-2749
dc.date.accessioned	2024-01-09T05:55:41Z
dc.date.available	2024-01-09T05:55:41Z
dc.date.issued	2023-08
dc.identifier.citation	Nonlinear Dynamics, 2023, 111, (16), pp. 15263-15278
dc.identifier.issn	0924-090X
dc.identifier.issn	1573-269X
dc.identifier.uri	http://hdl.handle.net/10453/174162
dc.description.abstract	<jats:title>Abstract</jats:title><jats:p>Dynamic pricing depends on the understanding of uncertain demand. We ask the question whether a stochastic system is sufficient to model this uncertainty. We propose a novel paradigm based on statistical analysis of recurrence quantification measures. The paradigm fits nonlinear dynamics by simultaneously optimizing both the determinism and the trapping time in recurrence plots and identifies an optimal time delay embedding. We firstly apply the paradigm on well-known deterministic and stochastic systems including Duffing systems and multi-fractional Gaussian noise. We then apply the paradigm to optimize the sampling of empirical point process data from RideAustin, a company providing ride share service in the city of Austin, Texas, the USA, thus reconstructing a period-7 attractor. Results show that in deterministic systems, an optimal embedding exists under which recurrence plots exhibit robust diagonal or vertical lines. However, in stochastic systems, an optimal embedding often does not exist, evidenced by the inability to shrink the standard deviation of either the determinism or the trapping time. By means of surrogate testing, we also show that a Poisson process or a stochastic system with periodic trend is insufficient to model uncertainty contained in empirical data. By contrast, the period-7 attractor dominates and well models nonlinear dynamics of empirical data via irregularly switching of the slow and the fast dynamics. Findings highlight the importance of fitting and recreating nonlinear dynamics of data in modeling practical problems.</jats:p>
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Nonlinear Dynamics
dc.relation.isbasedon	10.1007/s11071-023-08629-x
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	01 Mathematical Sciences, 09 Engineering
dc.subject.classification	Acoustics
dc.subject.classification	40 Engineering
dc.subject.classification	49 Mathematical sciences
dc.title	Recurrence-based reconstruction of dynamic pricing attractors
dc.type	Journal Article
utslib.citation.volume	111
utslib.for	01 Mathematical Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2024-01-09T05:55:39Z
pubs.issue	16
pubs.publication-status	Published
pubs.volume	111
utslib.citation.issue	16

Abstract:

AbstractDynamic pricing depends on the understanding of uncertain demand. We ask the question whether a stochastic system is sufficient to model this uncertainty. We propose a novel paradigm based on statistical analysis of recurrence quantification measures. The paradigm fits nonlinear dynamics by simultaneously optimizing both the determinism and the trapping time in recurrence plots and identifies an optimal time delay embedding. We firstly apply the paradigm on well-known deterministic and stochastic systems including Duffing systems and multi-fractional Gaussian noise. We then apply the paradigm to optimize the sampling of empirical point process data from RideAustin, a company providing ride share service in the city of Austin, Texas, the USA, thus reconstructing a period-7 attractor. Results show that in deterministic systems, an optimal embedding exists under which recurrence plots exhibit robust diagonal or vertical lines. However, in stochastic systems, an optimal embedding often does not exist, evidenced by the inability to shrink the standard deviation of either the determinism or the trapping time. By means of surrogate testing, we also show that a Poisson process or a stochastic system with periodic trend is insufficient to model uncertainty contained in empirical data. By contrast, the period-7 attractor dominates and well models nonlinear dynamics of empirical data via irregularly switching of the slow and the fast dynamics. Findings highlight the importance of fitting and recreating nonlinear dynamics of data in modeling practical problems.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/174162