Key Tactics The Pros Use For Feature Extraction From Time Series

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Python library tsfeature helps to compute a vector of features on each time series, measuring different characteristic-features of the series. The features may include lag correlation, the strength of seasonality, spectral entropy, etc. This enables the most unusual series, based on their feature vectors, to be identified. The bivariate outlier detection methods used are based on the highest density regions. The ARCH or Autoregressive Conditional Conditional Heteroskedasticity method plays a vital role in time-series highly volatile models.