 aar | Calculates adaptive autoregressive (AAR) and adaptive autoregressive moving average estimates (AARMA) |
| aarmam | Estimating Adaptive AutoRegressive-Moving-Average-and-mean model (includes mean term) |
| ac2poly | converts the autocorrelation sequence into an AR polynomial |
| ac2rc | converts the autocorrelation function into reflection coefficients |
| acorf | Calculates autocorrelations for multiple data series. |
| acovf | ACOVF estimates autocovariance function (not normalized) |
| adim | ADIM adaptive information matrix. Estimates the inverse |
| amarma | Adaptive Mean-AutoRegressive-Moving-Average model estimation |
| ar2poly | converts autoregressive parameters into AR polymials |
| ar2rc | converts autoregressive parameters into reflection coefficients |
| ar_spa | AR_SPA decomposes an AR-spectrum into its compontents |
| arcext | ARCEXT extracts AR and RC of order P from Matrix MX |
| arfit2 | ARFIT2 estimates multivariate autoregressive parameters |
| biacovf | BiAutoCovariance function |
| bisdemo | BISDEMO (script) Shows BISPECTRUM of eeg8s.mat |
| bispec | Calculates Bispectrum |
| content | Time Series Analysis (Ver 3.10) |
| contents | Time Series Analysis - A toolbox for the use with Matlab and Octave. |
| covm | COVM generates covariance matrix |
| demoperf | Demonstrates the much higher performance |
| detrend | DETREND removes the trend from data, NaN's are considered as missing values |
| durlev | function [AR,RC,PE] = durlev(ACF); |
| flag_implicit_samplerate | The use of FLAG_IMPLICIT_SAMPLERATE is in experimental state. |
| flix | floating point index - interpolates data in case of non-integer indices |
| hist2res | Evaluates Histogram data |
| hist2res2 | Evaluates Histogram data |
| histo | HISTO calculates histogram for each column |
| histo2 | HISTO2 calculates histogram of each column |
| histo3 | HISTO3 calculates histogram and performs data compression |
| histo4 | HISTO4 calculates histogram for rows and supports data compression |
| hup | HUP(C) tests if the polynomial C is a Hurwitz-Polynomial. |
| invest0 | First Investigation of a signal (time series) - automated part |
| invest1 | First Investigation of a signal (time series) - interactive |
| invfdemo | invfdemo demonstrates Inverse Filtering |
| lattice | Estimates AR(p) model parameter with lattice algorithm (Burg 1968) |
| lpc | LPC Linear prediction coefficients |
| mvaar | Multivariate (Vector) adaptive AR estimation base on a multidimensional |
| mvar | MVAR estimates Multi-Variate AutoRegressive model parameters |
| mvfilter | Multi-variate filter function |
| mvfreqz | MVFREQZ multivariate frequency response |
| pacf | Partial Autocorrelation function |
| parcor | estimates partial autocorrelation coefficients |
| poly2ac | converts an AR polynomial into an autocorrelation sequence |
| poly2ar | Converts AR polymials into autoregressive parameters. |
| poly2rc | converts AR-polynomial into reflection coefficients |
| rc2ac | converts reflection coefficients to autocorrelation sequence |
| rc2ar | converts reflection coefficients into autoregressive parameters |
| rc2poly | converts reflection coefficients into an AR-polynomial |
| rmle | RMLE estimates AR Parameters using the Recursive Maximum Likelihood |
| sbispec | SBISPEC show BISPECTRUM |
| selmo | Model order selection of an autoregrssive model |
| selmo2 | SELMO2 - model order selection for univariate and multivariate |
| sinvest1 | SINVEST1 shows the parameters of a time series calculated by INVEST1 |
| sumskipnan | SUMSKIPNAN adds all non-NaN values. |
| tsademo | TSADEMO demonstrates INVEST1 on EEG data |
| ucp | UCP(C) tests if the polynomial C is a Unit-Circle-Polynomial. |
| y2res | Y2RES evaluates basic statistics of a data series |
Master index