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On the Entropy Production of Time Series with Unidirectional Linearity




There are non-Gaussian time series that admit a causal linear autoregressive moving average (ARMA) model when regressing the future on the past, but not when regressing the past on the future. The reason is that, in the latter case, the regression residuals are not statistically independent of the regressor. In previous work, we have experimentally verified that many empirical time series indeed show such a time inversion asymmetry. For various physical systems, it is known that time-inversion asymmetries are linked to the thermodynamic entropy production in non-equilibrium states. Here we argue that unidirectional linearity is also accompanied by entropy generation. To this end, we study the dynamical evolution of a physical toy system with linear coupling to an infinite environment and show that the linearity of the dynamics is inherited by the forward-time conditional probabilities, but not by the backward-time conditionals. The reason is that the environment permanently provides particles that are in a product state before they interact with the system, but show statistical dependence afterwards. From a coarse-grained perspective, the interaction thus generates entropy. We quantitatively relate the strength of the non-linearity of the backward process to the minimal amount of entropy generation. The paper thus shows that unidirectional linearity is an indirect implication of the thermodynamic arrow of time, given that the joint dynamics of the system and its environment is linear.

Author(s): Janzing, D.
Journal: Journal of Statistical Physics
Volume: 138
Number (issue): 4-5
Pages: 767-779
Year: 2010
Month: March
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1007/s10955-009-9897-8
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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  title = {On the Entropy Production of Time Series with Unidirectional Linearity},
  author = {Janzing, D.},
  journal = {Journal of Statistical Physics},
  volume = {138},
  number = {4-5},
  pages = {767-779},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = mar,
  year = {2010},
  month_numeric = {3}