The intrinsic predictability of ecological time series and its potential to guide forecasting
dc.contributor.author
Pennekamp, Frank
dc.contributor.author
Iles, Alison C.
dc.contributor.author
Garland, Joshua
dc.contributor.author
Brennan, Georgina
dc.contributor.author
Brose, Ulrich
dc.contributor.author
Gaedke, Ursula
dc.contributor.author
Jacob, Ute
dc.contributor.author
Kratina, Pavel
dc.contributor.author
Matthews, Blake
dc.contributor.author
Munch, Stephan
dc.contributor.author
Novak, Mark
dc.contributor.author
Palamara, Gian Marco
dc.contributor.author
Rall, Björn C.
dc.contributor.author
Rosenbaum, Benjamin
dc.contributor.author
Tabi, Andrea
dc.contributor.author
Ward, Colette
dc.contributor.author
Williams, Richard
dc.contributor.author
Ye, Hao
dc.contributor.author
Petchey, Owen L.
dc.date.accessioned
2020-01-22T13:56:05Z
dc.date.available
2020-01-22T10:59:05Z
dc.date.available
2020-01-22T13:56:05Z
dc.date.issued
2019-05
dc.identifier.issn
0012-9615
dc.identifier.issn
1557-7015
dc.identifier.issn
1741-7015
dc.identifier.other
10.1002/ecm.1359
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/393006
dc.identifier.doi
10.3929/ethz-b-000393006
dc.description.abstract
Successfully predicting the future states of systems that are complex, stochastic, and potentially chaotic is a major challenge. Model forecasting error (FE) is the usual measure of success; however model predictions provide no insights into the potential for improvement. In short, the realized predictability of a specific model is uninformative about whether the system is inherently predictable or whether the chosen model is a poor match for the system and our observations thereof. Ideally, model proficiency would be judged with respect to the systems’ intrinsic predictability, the highest achievable predictability given the degree to which system dynamics are the result of deterministic vs. stochastic processes. Intrinsic predictability may be quantified with permutation entropy (PE), a model‐free, information‐theoretic measure of the complexity of a time series. By means of simulations, we show that a correlation exists between estimated PE and FE and show how stochasticity, process error, and chaotic dynamics affect the relationship. This relationship is verified for a data set of 461 empirical ecological time series. We show how deviations from the expected PE–FE relationship are related to covariates of data quality and the nonlinearity of ecological dynamics. These results demonstrate a theoretically grounded basis for a model‐free evaluation of a system's intrinsic predictability. Identifying the gap between the intrinsic and realized predictability of time series will enable researchers to understand whether forecasting proficiency is limited by the quality and quantity of their data or the ability of the chosen forecasting model to explain the data. Intrinsic predictability also provides a model‐free baseline of forecasting proficiency against which modeling efforts can be evaluated.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Wiley
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Empirical dynamic modelling
en_US
dc.subject
Forecasting
en_US
dc.subject
Information theory
en_US
dc.subject
Permutation entropy
en_US
dc.subject
Population dynamics
en_US
dc.subject
Time series analysis
en_US
dc.title
The intrinsic predictability of ecological time series and its potential to guide forecasting
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2019-01-23
ethz.journal.title
Ecological Monographs
ethz.journal.volume
89
en_US
ethz.journal.issue
2
en_US
ethz.journal.abbreviated
Ecol. monogr.
ethz.pages.start
e01359
en_US
ethz.size
17 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.publication.place
Hoboken, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02720 - Institut für Integrative Biologie / Institute of Integrative Biology::03705 - Jokela, Jukka / Jokela, Jukka
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02720 - Institut für Integrative Biologie / Institute of Integrative Biology::03705 - Jokela, Jukka / Jokela, Jukka
en_US
ethz.date.deposited
2020-01-22T10:59:12Z
ethz.source
BATCH
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-01-22T13:56:18Z
ethz.rosetta.lastUpdated
2022-03-29T00:49:17Z
ethz.rosetta.versionExported
true
ethz.COinS
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