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時系列解析

AR、ARMA、状態空間モデルなどを含む、線形および非線形のモデルを同定することで時系列データを解析し、値を予測する

関数

arEstimate parameters of AR model or ARI model for scalar time series
armaxEstimate parameters of ARMAX, ARIMAX, ARMA, or ARIMA model using time-domain data
arxEstimate parameters of ARX, ARIX, AR, or ARI model
etfeEstimate empirical transfer functions and periodograms
spaEstimate frequency response with fixed frequency resolution using spectral analysis
spafdrEstimate frequency response and spectrum using spectral analysis with frequency-dependent resolution
ivarAR model estimation using instrumental variable method
n4sidEstimate state-space model using subspace method with time-domain or frequency-domain data
ssestEstimate state-space model using time-domain or frequency-domain data
pemPrediction error estimate for linear and nonlinear model
nlarxEstimate parameters of nonlinear ARX model
idpolyPolynomial model with identifiable parameters
idssState-space model with identifiable parameters
idnlarxNonlinear ARX model
getpvecModel parameters and associated uncertainty data
setpvecModify value of model parameters
initSet or randomize initial parameter values
noise2measNoise component of model
spectrumOutput power spectrum of time series models
forecastForecast identified model output
simSimulate response of identified model
arOptionsOption set for ar
forecastOptionsOption set for forecast
simOptionsOption set for sim

例および操作のヒント

Estimate Time-Series Power Spectra

Estimate power spectra for time series data at the command line and in the app.

Estimate AR and ARMA Models

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

Estimate ARIMA Models

Estimate autoregressive integrated Moving Average (ARIMA) models.

Estimate State-Space Time Series Models

Estimate state-space models for time series data at the command line.

Identify Time Series Models at the Command Line

Simulate a time series and use parametric and nonparametric methods to estimate and compare time-series models.

Analyze Time-Series Models

Learn 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. 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.)

Forecast Output of 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. The predator-prey population-change dynamics are modeled using linear and nonlinear time series models. Forecasting performance of these models is compared.

Time Series Prediction and Forecasting for Prognosis

Create a time series model and use the model for prediction, forecasting, and state estimation.

概念

What Are 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.

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.

注目の例

加圧された変圧器からの電流信号のモデル化

加圧された変圧器からの電流信号のモデル化

この例では、測定された信号のモデル化を示します。400 kV の 3 相変圧器が加圧されたときの R 位相からの電流信号を解析します。測定はスウェーデンの Sydkraft AB 社によって実行されました。

スクリプトを開く

System Identification Toolbox ドキュメンテーション

サポート

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