|Estimate parameters of AR model or ARI model for scalar time series|
|Estimate parameters of ARMAX 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|
|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|
How to estimate power spectra for time series data in the app and at the command line.
How to estimate polynomial AR and ARMA models for time series data in the app and at the command line.
This example shows how to estimate Autoregressive Integrated Moving Average or ARIMA models.
How to estimate state-space models for time series data in the app and at the command line.
This example shows how to simulate a time series and use parametric and nonparametric methods to estimate and compare time-series models.
This example shows 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.
This example shows how to create a time series model and use the model for prediction, forecasting, and state estimation. The measured data is from an induction furnace whose slot size erodes over time. The slot size cannot be measured directly but the furnace current and consumed power are measured. It is known that as the slot size increases, the slot resistance decreases. The ratio of measured current squared to measured power is thus proportional to the slot size. You use the measured current-power ratio (both current and power measurements are noisy) to create a time series model and use the model to estimate the current slot size and forecast the future slot size. Through physical inspection the induction furnace slot size is known at some points in time.
Definition of time series models.
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.