catch22-0.4.0-5.fc39 >t 6 6_ ,06fx|3!92d LuZLd6. uZLNeJ+9pC$GB\H٥{n7}VDE0(MZ+?0iC~.ar9=x2 PCt٘Kj1~2Zp6nS!Ԧ0tN ʌTH[ܬ_aLkMw%"b #piD 2*ǝv.ԓ g6vD;tA'OhzPP0"0SwPI.E_O$%^a4mzdlצ. nyyZ[y5Y98~1 Ś\5C[pA(9yvh?(6` %+ص&rhFڅmqNݚDPPa؉4 r=>}U-o+ƺZ *9̦\ǪIJgx&k^oa<3vNxu!4m%ܔ# ōlc B)U|\tEnn>%ӄ;|u[dDCjݧ"롄~JacWbLe.9ʙE#fYIZ; $[|֟ R?lj*rAXhB,si2{tiGdD so_.DbeZjAl TbOb>Ly_iGDuE,C cz ԯr|֘'"GyE,ɟ2i4 L;Bfe$ȤMv!gxG] \ѕϚN|Tp=>`@?d   =dh  (  :  L  p  y     "@ d|;(e8p 9 : FNG H I XY\ ] ^b(deflt uD vhwt x y z#)lpCcatch220.4.05.fc39CAnonical Time-series CHaracteristics catch22 is a collection of 22 time-series features coded in C that can be run from Python, R, Matlab, and Julia. The catch22 features are a high-performing subset of the over 7000 features in hctsa. Features were selected based on their classification performance across a collection of 93 real-world time-series classification problems, as described in our open-access paper: - Lubba et al. (2019). catch22: CAnonical Time-series CHaracteristics (https://doi.org/10.1007/s10618-019-00647-x) The computational pipeline used to generate the catch22 feature set is in the op_importance (https://github.com/chlubba/op_importance) repository. 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