This　article　investigates　into　signal　regularity　in　ac　series　arc　fault　(SAF)　detection.　The　regularity　can　solve　the　vital　problem　that　SAF　current　characteristics　change　or　disappear　in　unknown　multiload　circuits.　A　coupling　method　is　adopted　to　capture　high-frequency　differential　signals.　The　coupling　signals　show　that　the　waveform　in　single-load　circuits　closely　resembles　the　waveform　in　multiload　ones.　However,　the　coupling　method　confuses　normal　signals　with　arcing　ones　in　dimmer　loads.　To　address　the　issue,　this　article　presents　a　time-series　reconstruction　method　based　on　spectral　features.　First,　the　spectral　features　are　analyzed　between　fault-like　signals　and　fault　signals.　According　to　the　spectral　features　and　the　desirable　margin,　the　time　series　is　self-adaptively　decomposed　and　reconstructed.　Then,　the　pulse-recognition　algorithm　is　used　to　extract　arcing　features　of　the　reconstructed　signals.　Finally,　the　detection　method　determined　by　single-load　circuits　is　used　to　identify　SAFs　in　unknown　multiload　ones.　The　results　show　the　presented　approach　has　good　generalization　performance　and　identification　precision　under　arbitrary　circuits.