Science Inventory

Using Fisher information to track stability in multivariate systems

Citation:

Ahmad, N., S. Derrible, T. Eason, AND H. Cabezas. Using Fisher information to track stability in multivariate systems. Royal Society Open Science. Royal Society Publishing, London, Uk, , 01-08, (2016).

Impact/Purpose:

Demonstrate Fisher information as a useful method for assessing patterns in big data.

Description:

With the current proliferation of data, the proficient use of statistical and mining techniques offer substantial benefits to capture useful information from any dataset. As numerous approaches make use of information theory concepts, here, we discuss how Fisher information (FI) can be applied to sustainability science problems and used in data mining applications by analyzing patterns in data. FI was developed as a measure of information content in data, and it has been adapted to assess order in complex system behaviors. The main advantage of the approach is the ability to collapse multiple variables into an index that can be used to assess stability and track overall trends in a system, including its regimes and regime shifts. Here, we provide a brief overview of FI theory, followed by a simple step-by-step numerical example on how to compute FI. Furthermore, we introduce an open source Python library that can be freely downloaded from GitHub and we use it in a simple case study to evaluate the evolution of FI for the global-mean temperature from 1880 to 2015. Results indicate significant declines in FI starting in 1978, suggesting a possible regime shift.

URLs/Downloads:

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Record Details:

Record Type: DOCUMENT (JOURNAL/PEER REVIEWED JOURNAL)
Product Published Date: 11/09/2016
Record Last Revised: 04/28/2017
OMB Category: Other
Record ID: 335871

Organization:

U.S. ENVIRONMENTAL PROTECTION AGENCY

OFFICE OF RESEARCH AND DEVELOPMENT

NATIONAL RISK MANAGEMENT RESEARCH LABORATORY

SUSTAINABLE TECHNOLOGY DIVISION

SYSTEMS ANALYSIS BRANCH