We introduce the new time series analysis features of scikits.statsmodels. This includes descriptive statistics, statistical tests and several linear model classes, autoregressive, AR, autoregressive moving-average, ARMA, and vector autoregressive models VAR.

We examine the problem of accurate detection and classification of artifacts in continuous EEG recordings. Manual identification of artifacts, by means of an expert or panel of experts, can be tedious, time-consuming and infeasible for large datasets. We use autoregressive (AR) models for feature extraction and characterization of EEG signals containing several kinds of subject-generated artifa...

The subject of this paper is autoregressive (AR) modeling of a stationary, Gaussian discrete time process, based on a finite sequence of observations. The process is assumed to admit an AR(∞) representation with exponentially decaying coefficients. We adopt the nonparametric minimax framework and study how well the process can be approximated by a finiteorder AR model. A lower bound on the accu...

Hidden Markov modeling of speech waveforms using structured covariance matrices is studied and applied to recognition of clean and noisy speech signals. This technique allows for easier model adaptation in additive noise than does cepstral modeling of speech. Waveform modeling using autoregressive (AR) structured covariances has been extensively studied and applied previously. However, other co...

Best practice life expectancy has recently been modeled using extreme value theory. In this paper we present the Gumbel autoregressive model of order one—Gumbel AR(1)—as an option for modeling best expectancy. This class represents a neat and coherent framework time series extremes. The distribution accounts nature expectancy, while AR structure temporal dependence in series. Model diagnostics ...

Autoregressive (AR) modeling by linear prediction (LP) provides the basis of a wide variety of signal processing and communication systems including parametric spectral estimation and system identification. Perhaps the greatest success of linear prediction techniques is to be found in speech analysis and audio coding. In this paper, we first reviewed the general frameworks of predictive signal ...

In longitudinal studies, serial dependence of repeated outcomes must be taken into account to make correct inferences on covariate effects. As such, care must be taken in modeling the covariance matrix. However, estimation of the covariance matrix is challenging because there are many parameters in the matrix and the estimated covariance matrix should be positive definite. To overcomes these li...

In Time-Division-Duplex (TDD) wireless communications, downlink beamforming performance of a smart antenna system can be degraded due to variation of spatial signature vectors in vehicular scenarios. To mitigate this, downlink beams must be adjusted according to changing propagation dynamics. This can be achieved by modeling spatial signature vectors in the uplink period and then predicting the...

Blind audio source separation (BASS) arises in a number of applications in speech and music processing such as speech enhancement, speaker diarization, automated music transcription etc. Generally, BASS methods consider multichannel signal capture. The single microphone case is the most difficult underdetermined case, but it often arises in practice. In the approach considered here, the main so...

We present a new method for estimation of spectrum transition of nonstationary signals in cases of low signal-to-noise ratio (SNR). Instead of the basic functions employed in the previously proposed time-varying autoregressive (AR) modeling, we introduce a spectrum transition constraint into the cost function described by the partial correlation (PARCORR) coefficients so that the method is appl...