University of Florida

ABE 6933
Data Diagnostics:

Detecting and Characterizing Deterministic Structure in Time Series Data

Semester Taught - Fall

Description

Credits: 3

There is growing concern that models used in public decision-making fail to capture complex real-world dynamics exhibited by observed data.  For example, Congress recently held a special hearing to investigate why conventional macroeconomic models developed with federal funding failed to anticipate the current financial crisis. In another example, Milly et al. (2008) criticized water-management models based on time-invariant averages when “anthropogenic change of Earth’s climate is altering the means and extremes of precipitation, evapotranspiration, and rates of discharge of rivers...beyond the range of historical behaviors.”

Model builders must become thoroughly acquainted with available data to construct informative models that successfully simulate complex real-world behavior.  Fortunately, the last few decades have seen substantial advances in data diagnostic techniques that can guide construction and testing of theory-based models.

 

This class focuses on Singular Spectrum Analysis, Phase Space Reconstruction, Surrogate Data Analysis, and Convergent Cross MappingSingular Spectrum Analysis (SSA) filters noise from observed time series data by decomposing the data into a sum of structural (trend and oscillations) and structureless-noise components.  It can be used to fill in missing data observations, identify critical turning points, and make forecasts.  Phase Space Reconstruction uses the SSA-filtered data to identify, characterize, and reconstruct deterministic dynamics of the real-world system that generated the data.  Surrogate Data Analysis provides a nonparametric statistical test for determining whether apparent deterministic structure uncovered in Phase Space Reconstruction is the figment of a mimicking stochastic process.  Convergent Cross Mapping extends Phase Space Reconstruction to test for causal relationships among a collection of time series variables. Finally, Extreme Value Statistics uses the SSA structureless-noise component to statistically model discrepancies between the observed data and the SSA-filtered model.

Pre-requisites

Elementary Statistics and Differential Equations

Course Objective

The major objective is to give students hands-on experience and confidence in employing data diagnostic techniques in applied research.  

Instructor

Dr. Ray Huffaker
Office: 1167 McCarty A
Phone: 392-1826 x204
rhuffaker@ufl.edu

Material/Supply Fees

None.

Class Materials Required

No textbook required.

Most readings are journal articles that can be downloaded from the UF library webpage.  Other readings are provided by the instructor.  Most software required by the class can be downloaded at no cost.  Help documents are readily available to learn programming basics.  Students are expected to bring personal laptop computers to class.

Method

The course takes a ‘workshop’ approach to learning.  The instructor will cover the background information required to understand how and why diagnostic methods work.  Students will run these methods together in and outside of class to analyze several biophysical time series.  This requires that students bring their personal laptops to class, and dedicate time outside of class to familiarize themselves with programming basics.  The class will study journal articles applying diagnostic methods to learn how they are employed in applied research.  Near the end of the course, each student will present a PowerPoint presentation of a diagnosed time series, and submit a 5-page (single-spaced) research proposal formulating an interesting research question.

    

Course Outline

Topics (click for complete syllabus details)

Topics

Introductory Overview of Time-Series Data Diagnostics

Spectral Analysis

Singular Spectrum Analysis

Phase Space Reconstruction

Surrogate Data Analysis

Convergent Cross Mapping (Identify Causal Networks)

Extreme Value Statistics

Grading

Final grade based on class attendance, in-class participation, PowerPoint presentation, and research proposal.

Academic Honesty

All students admitted to the University of Florida have signed a statement of academic honesty committing themselves to be honest in all academic work and understanding that failure to comply with this commitment will result in disciplinary action. This statement is a reminder to uphold your obligation as a UF student and to be honest in all work submitted and exams taken in this course and all others.

Accommodation for Students with Disabilities

Students requesting classroom accommodation must first register with the Dean of Students Office. That office will provide the student with documentation that he/she must provide to the course instructor when requesting accommodation.

Use of Library, Personal References, PC Programs and Electronic Databases

These items are university property and should be utilized with other users in mind. Never remove, mark, modify nor deface resources that do not belong to you. If you're in the habit of underlining text, do it only on your personal copy. It is inconsiderate, costly to others, and dishonest to use common references otherwise.

Software Use

All faculty, staff and students of the University are required and expected to obey the laws and legal agreements governing software use. Failure to do so can lead to monetary damages and/or criminal penalties for the individual violator. Because such violations are also against University policies and rules, disciplinary action will be taken as appropriate. We, the members of the University of Florida community, pledge to hold ourselves and our peers to the highest standards of honesty and integrity.

UF Counseling Services

Resources are available on-campus for students having personal problems or lacking clear career and academic goals which interfere with their academic performance. These resources include:

  1. University Counseling Center, 301 Peabody Hall, 392-1575, personal and career counseling;
  2. Student Mental Health, Student Health Care Center, 392-1171, personal counseling;
  3. Center for Sexual Assault/Abuse Recovery and Education (CARE), Student Health Care Center, 392-1161, sexual assault counseling;
  4. Career Resource Center, Reitz Union, 392-1601, career development assistance and counseling.