Indicator Description Reference Averaging or dimension reduction technique
Mean SD (Standard Deviation) Average variance across all time series representing the system. (Dakos 2018) Average
Max SD The variance of the time series with the highest variance of all assessed time series. (Dakos 2018) Average
Mean AR1 (Autocorrelation at lag1) Average autocorrelation across all time series representing the system. (Dakos 2018) Average
Max AR1 The autocorrelation of the time series with the highest autocorrelation of all assessed time series. (Dakos 2018) Average
Dominant MAF (maximum autocorrelation factor) eigenvalue The minimum eigenvalue of the system following MAF dimension reduction. (Weinans et al. 2019) Dimension reduction
MAF AR1 The autocorrelation of the data projected on to the first MAF – i.e. the autocorrelation of the first MAF. (Weinans et al. 2019) Dimension reduction
MAF SD The variance of the data projected on to the first MAF – i.e. the variance of the first MAF. (Weinans et al. 2019) Dimension reduction
First PC (principal component) AR1 The autocorrelation of the data projected on to the first PC – i.e. the autocorrelation of the first PC. (Held and Kleinen 2004) Dimension reduction
First PC SD/ Explained variance The variance of the data projected on to the first PC – i.e. the variance of the first PC. (Held and Kleinen 2004) Dimension reduction
Dominant eigenvalue of the covariance matrix The maximum eigenvalue of the covariance matrix between all representative time series. (Chen et al. 2019) Neither
Maximum covariance The maximum value of the covariance matrix between all representative time series. (Suweis and D’Odorico 2014) Neither
Mutual information A measurement of multi-information or how much each time series informs on the others. (Quax et al. 2013) Neither