time series analysis -凯发k8网页登录

analyze time series data by identifying linear and nonlinear models such as ar, arma, state-space, and grey-box models, performing spectral analysis, and forecasting model outputs

a time series is data that contains one or more measured output channels but no measured input. a time series model, also called a signal model, is a dynamic system that is identified to fit a given signal or time series data. the time series can be multivariate, which leads to multivariate models. you can identify time series models in the system identification app or at the command line. system identification toolbox™ enables you to create and estimate four general types of time series model.

linear parametric models — estimate parameters in structures such as autoregressive models and state-space models.
frequency-response models — estimate spectral models using spectral analysis.
nonlinear arx models — estimate parameters in the nonlinear arx structure.
grey-box models — estimate the coefficients of the ordinary differential or difference equations that represent your system dynamics.

parametric time series model identification requires uniformly sampled time-domain data, except for the arx model, which can handle frequency-domain signals. spectral analysis algorithms support time-domain and frequency-domain data. your data can have one or more output channels and must have no input channel. for more information on time series models, see

you can use the identified models to predict model output at the command line, in the app, or in simulink^®. at the command line, you can also forecast model outputs beyond the time range of the measured data.

functions

estimation

	estimate parameters when identifying ar model or ari model for scalar time series
	option set for `ar`
	estimate parameters of arx, arix, ar, or ari model
	estimate parameters of armax, arimax, arma, or arima model using time-domain data
	ar model estimation using instrumental variable method
	estimate state-space model using time-domain or frequency-domain data
	estimate state-space model using subspace method with time-domain or frequency-domain data
	estimate frequency response with fixed frequency resolution using spectral analysis
	estimate frequency response and spectrum using spectral analysis with frequency-dependent resolution
	estimate empirical transfer functions and periodograms
	estimate parameters of nonlinear arx model
	estimate ode parameters of linear grey-box model
	estimate nonlinear grey-box model parameters

models

	polynomial model with identifiable parameters
	state-space model with identifiable parameters
	frequency response data or model
	nonlinear arx model
	linear ode (grey-box model) with identifiable parameters
	nonlinear grey-box model

response analysis

	plot or return output power spectrum of time series model or disturbance spectrum of linear input/output model
	forecast identified model output
	predict identified model k-step-ahead output

topics

about time series models

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.
learn how to analyze time series models.

estimate models

simulate a time series and use parametric and nonparametric methods to estimate and compare time-series models.
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.
estimate power spectra for time series data at the command line and in the app.
estimate model parameters using linear and nonlinear grey-box modeling.

forecast model output

workflow for forecasting time series data and input-output data using linear and nonlinear models.
create a time series model and use the model for prediction, forecasting, and state estimation.
understand the concept of forecasting data using linear and nonlinear models.