Once the attributes of observed time series data are identified, they can be interpreted, integrated with other data, and used for anomaly detection, forecasting, and other machine learning tasks.

An approach to time series model identification is described which involves the simultaneous use of frequency, time and quantile domain algorithms; the approach is called quantile spectral analysis.

Classical spectral methods are subject to two fundamental limitations: they can account only for covariance-related serial dependences, and they require second-order stationarity. Much attention has ...