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Time Series Analysis

Analyze time series data by identifying linear and nonlinear models, including AR, ARMA, and state-space models; forecast values

Functions

ar Estimate parameters of AR model for scalar time series
armax Estimate parameters of ARMAX model using time-domain data
arx Estimate parameters of ARX or AR model using least squares
etfe Estimate empirical transfer functions and periodograms
spa Estimate frequency response with fixed frequency resolution using spectral analysis
spafdr Estimate frequency response and spectrum using spectral analysis with frequency-dependent resolution
ivar AR model estimation using instrumental variable method
n4sid Estimate state-space model using subspace method
ssest Estimate state-space model using time or frequency domain data
pem Prediction error estimate for linear and nonlinear model
nlarx Estimate parameters of nonlinear ARX model
idpoly Polynomial model with identifiable parameters
idss State-space model with identifiable parameters
idnlarx Nonlinear ARX model
getpvec Model parameters and associated uncertainty data
setpvec Modify value of model parameters
init Set or randomize initial parameter values
noise2meas Noise component of model
spectrum Output power spectrum of time series models
forecast Forecast identified model output
sim Simulate response of identified model
arOptions Option set for ar
forecastOptions Option set for forecast
simOptions Option set for sim

Examples and How To

Estimate Time-Series Power Spectra

How to estimate power spectra for time series data in the app and at the command line.

Estimate AR and ARMA Models

How to estimate polynomial AR and ARMA models for time series data in the app and at the command line.

Estimate ARIMA Models

This example shows how to estimate Autoregressive Integrated Moving Average or ARIMA models.

Estimate State-Space Time Series Models

How to estimate state-space models for time series data in the app and at the command line.

Identify Time-Series Models at the Command Line

This example shows how to simulate a time-series model, compare the spectral estimates, estimate covariance, and predict output of the model.

Analyze Time-Series Models

This example shows how to analyze time-series models.

Spectrum Estimation Using Complex Data - Marple's Test Case

This example shows how to perform spectral estimation on time series data.

Forecast the Output of a Dynamic System

Workflow for forecasting time series data and input-output data using linear and nonlinear models.

Perform Multivariate Time Series Forecasting

This example shows how to perform multivariate time series forecasting of data measured from predator and prey populations in a prey crowding scenario.

Time Series Prediction and Forecasting for Prognosis

This example shows how to create a time series model and use the model for prediction, forecasting, and state estimation.

Concepts

What Are Time Series Models?

Definition of time series models.

Preparing Time-Series Data

Where you can learn more about importing and preparing time series data for modeling.

Introduction to Forecasting of Dynamic System Response

Understand the concept of forecasting data using linear and nonlinear models.

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