statsmodels.tsa System: Difference between revisions
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=== 2016 === | === 2016 === | ||
* http://www.statsmodels.org/dev/tsa.html | * http://www.statsmodels.org/dev/tsa.html | ||
** QUOTE: [[statsmodels.tsa]] contains model classes and functions that are useful for time series analysis. Basic models include univariate autoregressive models (AR), vector autoregressive models (VAR) and univariate autoregressive moving average models (ARMA). Non-linear models include Markov switching dynamic regression and autoregression. It also includes descriptive statistics for time series, for example autocorrelation, partial autocorrelation function and periodogram, as well as the corresponding theoretical properties of ARMA or related processes. It also includes methods to work with autoregressive and moving average lag-polynomials. Additionally, related statistical tests and some useful helper functions are available. <P> Estimation is either done by exact or conditional Maximum Likelihood or conditional least-squares, either using Kalman Filter or direct filters. <P> Currently, functions and classes have to be imported from the corresponding module, but the main classes will be made available in the statsmodels.tsa namespace. The module structure is within statsmodels.tsa is | ** QUOTE: [[statsmodels.tsa System|statsmodels.tsa]] contains model classes and functions that are useful for time series analysis. Basic models include univariate autoregressive models (AR), vector autoregressive models (VAR) and univariate autoregressive moving average models (ARMA). Non-linear models include Markov switching dynamic regression and autoregression. It also includes descriptive statistics for time series, for example autocorrelation, partial autocorrelation function and periodogram, as well as the corresponding theoretical properties of ARMA or related processes. It also includes methods to work with autoregressive and moving average lag-polynomials. Additionally, related statistical tests and some useful helper functions are available. <P> Estimation is either done by exact or conditional Maximum Likelihood or conditional least-squares, either using Kalman Filter or direct filters. <P> Currently, functions and classes have to be imported from the corresponding module, but the main classes will be made available in the statsmodels.tsa namespace. The module structure is within statsmodels.tsa is | ||
*** stattools : empirical properties and tests, acf, pacf, granger-causality, adf unit root test, kpss test, bds test, ljung-box test and others. | *** stattools : empirical properties and tests, acf, pacf, granger-causality, adf unit root test, kpss test, bds test, ljung-box test and others. | ||
ar_model : univariate autoregressive process, estimation with conditional and exact maximum likelihood and conditional least-squares | ar_model : univariate autoregressive process, estimation with conditional and exact maximum likelihood and conditional least-squares | ||
Revision as of 20:45, 23 December 2019
A statsmodels.tsa System is a Univariate Timeseries Modeling System within statsmodels.
- Context:
- ...
- Example(s):
- v??
- See: Multivariate Timeseries Analysis.
References
2016
- http://www.statsmodels.org/dev/tsa.html
- QUOTE: statsmodels.tsa contains model classes and functions that are useful for time series analysis. Basic models include univariate autoregressive models (AR), vector autoregressive models (VAR) and univariate autoregressive moving average models (ARMA). Non-linear models include Markov switching dynamic regression and autoregression. It also includes descriptive statistics for time series, for example autocorrelation, partial autocorrelation function and periodogram, as well as the corresponding theoretical properties of ARMA or related processes. It also includes methods to work with autoregressive and moving average lag-polynomials. Additionally, related statistical tests and some useful helper functions are available.
Estimation is either done by exact or conditional Maximum Likelihood or conditional least-squares, either using Kalman Filter or direct filters.
Currently, functions and classes have to be imported from the corresponding module, but the main classes will be made available in the statsmodels.tsa namespace. The module structure is within statsmodels.tsa is
- stattools : empirical properties and tests, acf, pacf, granger-causality, adf unit root test, kpss test, bds test, ljung-box test and others.
- QUOTE: statsmodels.tsa contains model classes and functions that are useful for time series analysis. Basic models include univariate autoregressive models (AR), vector autoregressive models (VAR) and univariate autoregressive moving average models (ARMA). Non-linear models include Markov switching dynamic regression and autoregression. It also includes descriptive statistics for time series, for example autocorrelation, partial autocorrelation function and periodogram, as well as the corresponding theoretical properties of ARMA or related processes. It also includes methods to work with autoregressive and moving average lag-polynomials. Additionally, related statistical tests and some useful helper functions are available.
ar_model : univariate autoregressive process, estimation with conditional and exact maximum likelihood and conditional least-squares
arima_model : univariate ARMA process, estimation with conditional and exact maximum likelihood and conditional least-squares
vector_ar, var : vector autoregressive process (VAR) estimation models, impulse response analysis, forecast error variance decompositions, and data visualization tools
kalmanf : estimation classes for ARMA and other models with exact MLE using Kalman Filter
arma_process : properties of arma processes with given parameters, this includes tools to convert between ARMA, MA and AR representation as well as acf, pacf, spectral density, impulse response function and similar
sandbox.tsa.fftarma : similar to arma_process but working in frequency domain
tsatools : additional helper functions, to create arrays of lagged variables, construct regressors for trend, detrend and similar.
filters : helper function for filtering time series
regime_switching : Markov switching dynamic regression and autoregression models