|Estimate parameters of AR model or ARI model for scalar time series|
|Estimate parameters of ARMAX, ARIMAX, ARMA, or ARIMA model using time-domain data|
|Estimate parameters of ARX, ARIX, AR, or ARI model|
|Estimate empirical transfer functions and periodograms|
|Estimate frequency response with fixed frequency resolution using spectral analysis|
|Estimate frequency response and spectrum using spectral analysis with frequency-dependent resolution|
|AR model estimation using instrumental variable method|
|Estimate state-space model using subspace method with time-domain or frequency-domain data|
|Estimate state-space model using time-domain or frequency-domain data|
|Prediction error estimate for linear and nonlinear model|
|Estimate parameters of nonlinear ARX model|
|Polynomial model with identifiable parameters|
|State-space model with identifiable parameters|
|Nonlinear ARX model|
|Model parameters and associated uncertainty data|
|Modify value of model parameters|
|Set or randomize initial parameter values|
|Noise component of model|
|Output power spectrum of time series models|
|Forecast identified model output|
|Simulate response of identified model|
Estimate power spectra for time series data at the command line and in the app.
Estimate polynomial AR and ARMA models for time series data at the command line and in the app.
Estimate autoregressive integrated Moving Average (ARIMA) models.
Estimate state-space models for time series data at the command line.
Simulate a time series and use parametric and nonparametric methods to estimate and compare time-series models.
Learn how to analyze time series models.
This example shows how to perform spectral estimation on time series data. We use Marple's test case (The complex data in L. Marple: S.L. Marple, Jr, Digital Spectral Analysis with Applications, Prentice-Hall, Englewood Cliffs, NJ 1987.)
Workflow for forecasting time series data and input-output data using linear and nonlinear models.
This example shows how to perform multivariate time series forecasting of data measured from predator and prey populations in a prey crowding scenario. The predator-prey population-change dynamics are modeled using linear and nonlinear time series models. Forecasting performance of these models is compared.
Create a time series model and use the model for prediction, forecasting, and state estimation.
A time series model, also called a signal model, is a dynamic system that is identified to fit data that includes only output channels and no input channels.
Where you can learn more about importing and preparing time series data for modeling.
Understand the concept of forecasting data using linear and nonlinear models.