Measures
Time series metrics or features that can be used for time series classification or regression analysis:
- Univariate linear measures
- Moment (mathematics)
- Spectral band power
- Spectral edge frequency
- Accumulated Energy (signal processing)
- Characteristics of the autocorrelation function
- Hjorth parameters
- FFT parameters
- Autoregressive model parameters
- Univariate non-linear measures
- Measures based on the correlation sum
- Correlation dimension
- Correlation integral
- Correlation density
- Correlation entropy
- Approximate Entropy
- Sample Entropy
- Fourier entropy
- Wavelet entropy
- Rényi entropy
- Higher-order methods
- Marginal predictability
- Dynamical similarity index
- State space dissimilarity measures
- Lyapunov exponent
- Permutation methods
- Local flow
- Other univariate measures
- Algorithmic complexity
- Kolmogorov complexity estimates
- Hidden Markov Model states
- Surrogate time series and surrogate correction
- Loss of recurrence (degree of non-stationarity)
- Bivariate linear measures
- Maximum linear cross-correlation
- Linear Coherence (signal processing)
- Bivariate non-linear measures
- Non-linear interdependence
- Dynamical Entrainment (physics)
- Measures for Phase synchronization
- Similarity measures:
- Dynamic Time Warping
- Hidden Markov Models
- Edit distance
- Total correlation
- Newey–West estimator
- Prais-Winsten transformation
- Data as Vectors in a Metrizable Space
- Minkowski distance
- Mahalanobis distance
- Data as Time Series with Envelopes
- Global Standard Deviation
- Local Standard Deviation
- Windowed Standard Deviation
- Data Interpreted as Stochastic Series
- Pearson product-moment correlation coefficient
- Spearman's rank correlation coefficient
- Data Interpreted as a Probability Distribution Function
- Kolmogorov-Smirnov test
- Cramér-von Mises criterion
Read more about this topic: Time Series, Models
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