This is machine translation

Translated by

Mouseover text to see original. Click the button below to return to the English version of the page.

Note: This page has been translated by MathWorks. Click here to see
To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

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.

Forecast Multivariate Time Series

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.

Featured Examples

Modeling Current Signal From an Energizing Transformer

This example shows the modeling of a measured signal. We analyze the current signal from the R-phase when a 400 kV three-phase transformer is energized. The measurements were performed by Sydkraft AB in Sweden.

Open Script