Time-Series Analysis J. C. (Clint) Sprott 3/31/2017 J. C. (Clint) Sprott Department of Physics University of Wisconsin - Madison Workshop presented at the 2004 SCTPLS Annual Conference at Marquette University on July 15, 2004 Entire presentation available on WWW

Agenda Introductory lecture Hands-on tutorial Strange attractors – Break – Individual exploration Closing comments

Motivation Many quantities in nature fluctuate in time. Examples are the stock market, the weather, seismic waves, sunspots, heartbeats, and plant and animal populations. Until recently it was assumed that such fluctuations are a consequence of random and unpredictable events. With the discovery of chaos, it has come to be understood that some of these cases may be a result of deterministic chaos and hence predictable in the short term and amenable to simple modeling. Many tests have been developed to determine whether a time series is random or chaotic, and if the latter, to quantify the chaos. If chaos is found, it may be possible to improve the short-term predictability and enhance understanding of the governing process.

Goals This workshop will provide examples of time-series data from real systems as well as from simple chaotic models. A variety of tests will be described including linear methods such as Fourier analysis and autoregression, and nonlinear methods using state-space reconstruction. The primary methods for nonlinear analysis include calculation of the correlation dimension and largest Lyapunov exponent, as well as principal component analysis and various nonlinear predictors. Methods for detrending, noise reduction, false nearest neighbors, and surrogate data tests will be explained. Participants will use the "Chaos Data Analyzer" program to analyze a variety of typical time-series records and will learn to distinguish chaos from colored noise and to avoid the many common pitfalls that can lead to false conclusions. No previous knowledge or experience is assumed.

Precautions More art than science No sure-fire methods Easy to fool yourself Many published false claims Must use multiple tests Conclusions seldom definitive Compare with surrogate data Must ask the right questions “Is it chaos?” too simplistic

Applications Prediction Noise reduction Scientific insight Control

Examples Weather data Climate data Tide levels Seismic waves Cepheid variable stars Sunspots Financial markets Ecological fluctuations EKG and EEG data …

(Non-)Time Series Core samples Terrain features Sequence of letters in written text Notes in a musical composition Bases in a DNA molecule Heartbeat intervals Dripping faucet Necker cube flips Eye fixations during a visual task ...

Methods Linear (traditional) Nonlinear (chaotic) Fourier Analysis Autocorrelation ARMA LPC … Nonlinear (chaotic) State space reconstruction Correlation dimension Lyapunov exponent Principle component analysis Surrogate data …

Resources

Hierarchy of Dynamical Behaviors

Typical Experimental Data 3/31/2017 Typical Experimental Data 5 x Not usually shown in textbooks Could be: Plasma fluctuations Stock market data Meteorological data EEG or EKG Ecological data etc... Until recently, no hope of detailed understanding Could be an example of deterministic chaos -5 Time 500

Stationarity

Detrending

Detrended

Case Study

First Return Map

Time-Delayed Embedding Space Plot x(t) vs. x(t-), x(t-2), x(t-3), … Embedding dimension is # of delays Must choose  and dim carefully Orbit does not fill the space Diffiomorphic to actual orbit Dim of orbit = min # of variables x(t) can be any measurement fcn

Measurement Functions Xn+1 = 1 – 1.4X2 + 0.3Yn Yn+1 = Xn Hénon map:

Correlation Dimension N(r)  rD2 D2 = dlogN(r)/dlogr

Inevitable Ambiguity

Lyapunov Exponent  Rn =  R0en  = <ln|Rn/R0|>

Principal Component Analysis x(t)

State-space Prediction

Surrogate Data Original time series Shuffled surrogate Phase randomized

General Strategy Verify integrity of the data Test for stationarity Look at return maps, etc. Look at autocorrelation function Look at power spectrum Calculate correlation dimension Calculate Lyapunov exponent Compare with surrogate data sets Construct models Make predictions from models

Tutorial using CDA

Types of Attractors Examples: simple damped pendulum 3/31/2017 Types of Attractors Limit Cycle Fixed Point Focus Node Torus Strange Attractor Examples: simple damped pendulum driven mass on a spring inner tube

Strange Attractors Occur in infinite variety (like snowflakes) 3/31/2017 Strange Attractors Limit set as t   Set of measure zero Basin of attraction Fractal structure non-integer dimension self-similarity infinite detail Chaotic dynamics sensitivity to initial conditions topological transitivity dense periodic orbits Aesthetic appeal Occur in infinite variety (like snowflakes) Produced many millions, looked at over 100,000 Like pornography, know it when you see it

Individual Exploration using CDA

Practical Considerations Calculation speed Required number of data points Required precision of the data Noisy data Multivariate data Filtered data Missing data Nonuniformly sampled data Nonstationary data

Some General High-Dimensional Models Fourier Series: Linear Autoregression: (ARMA, LPC, MEM…) Nonlinear Autogression: (Polynomial Map) Neural Network:

Artificial Neural Network

Simple models may suffice Summary 3/31/2017 Nature is complex Simple models may suffice but

3/31/2017 References http://sprott.physics.wisc.edu/lec tures/tsa.ppt (this presentation) http://sprott.physics.wisc.edu/cd a.htm (Chaos Data Analyzer) sprott@physics.wisc.edu (my email)