Automatic spectral analysis with time series models

Automatic spectral analysis with time series models

The increased computational speed and developments in the robustness of algorithms have created the possibility to identify automatically a well-fitting time series model for stochastic data. It is possible to compute more than 500 models and to select only one, which certainly is one of the better models, if not the very best. That model characterizes the spectral density of the data. Time ser...

Spectral Analysis of Irregularly Sampled Data with Time Series Models

Slotted resampling transforms an irregularly sampled process into an equidistant missing-data problem. Equidistant resampling inevitably causes bias, due to aliasing and the shift of the irregular observation times to an equidistant grid. Taking a slot width smaller than the resampling time can diminish the shift bias. A dedicated estimator for time series models of multiple slotted data sets w...

Automatic Music Tagging With Time Series Models

We present a system for automatic music annotation that leverages temporal (e.g., rhythmical) aspects as well as timbral content. Our system estimates a dynamic texture mixture (DTM) density over times series of acoustic features (instead of on individual features) for each tag in a semantic vocabulary. When analyzing a new song, our system processes the time series of acoustic features of the ...

SPECTRAL ANALYSIS of TIME SERIES

Time-dependent spectral analysis of epidemiological time-series with wavelets.

In the current context of global infectious disease risks, a better understanding of the dynamics of major epidemics is urgently needed. Time-series analysis has appeared as an interesting approach to explore the dynamics of numerous diseases. Classical time-series methods can only be used for stationary time-series (in which the statistical properties do not vary with time). However, epidemiol...