Measuring time series predictability using support vector regression

Most studies involving statistical time series analysis rely on assumptions of linearity, which by its simplicity facilitates parameter interpretation and estimation. However, the linearity assumption may be too restrictive for many practical applications. The implementation of nonlinear models in time series analysis involves the estimation of a large set of parameters, frequently leading to overfitting problems. In this article, a predictability coefficient is estimated using a combination of nonlinear autoregressive models and the use of support vector regression in this model is explored. We illustrate the usefulness and interpretability of results by using electroencephalographic records of an epileptic patient.

A Time-Series Analysis of the H alpha Emission Line in V3885 Sagitarii

Flickering is a phenomenon related to mass accretion observed among many classes of astrophysical objects. In this paper we present a study of flickering emission lines and the continuum of the cataclysmic variable V3885 Sgr. The flickering behavior was first analyzed through statistical analysis and the power spectra of lightcurves. Autocorrelation techniques were then employed to estimate the flickering timescale of flares. A cross-correlation study between the line and its underlying continuum variability is presented. The cross-correlation between the photometric and spectroscopic data is also discussed. Periodograms, calculated using emission-line data, show a behavior that is similar to those obtained from photometric datasets found in the literature, with a plateau at lower frequencies and a power-law at higher frequencies. The power-law index is consistent with stochastic events. The cross-correlation study indicates the presence of a correlation between the variability on Ha and its underlying continuum. Flickering timescales derived from the photometric data were estimated to be 25 min for two lightcurves and 10 min for one of them. The average timescales of the line flickering is 40 min, while for its underlying continuum it drops to 20 min.

Stabilizing Nonstationary Electrochemical Time Series

Electrochemical systems are ideal working-horses for studying oscillatory dynamics. Experimentally obtained time series, however, are usually associated with a spontaneous drift in some uncontrollable parameter that triggers transitions among different oscillatory patterns, despite the fact that all controllable parameters are kept constant. Herein we present an empirical method to stabilize experimental potential time series. The method consists of applying a negative galvanodynamic sweep to compensate the spontaneous drift and was tested for the oscillatory electro-oxidation of methanol on platinum. For a wide range of applied currents, the base system presents spontaneous transitions from quasi-harmonic to mixed mode oscillations. Temporal patterns were stabilized by galvanodynamic sweeps at different rates. The procedure resulted in a considerable increase in the number of oscillatory cycles from 5 to 20 times, depending on the specific temporal pattern. The spontaneous drift has been associated with uncompensated oscillations, in which the coverage of some adsorbed species are not reestablished after one cycle; i.e., there is a net accumulation and/or depletion of adsorbed species during oscillations. We interpreted the rate of the galvanodynamic sweep in terms of the time scales of the poisoning processes that underlies the uncompensated oscillations and thus the spontaneous slow drift.