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+of+this+type+of+time+series%252C+called+the+semi-stationary+time+series%252C+exhibits+that+a+finite+number+of+different+causal+mechanisms+occur+sequentially+and+periodically+across+time.+This+model+holds+considerable+practical+utility+because+it+can+represent+periodicity%252C+including+common+occurrences+such+as+seasonality+and+diurnal+variation.+We+propose+a+constraint-based%252C+non-parametric+algorithm+for+discovering+causal+relations+in+this+setting.+The+resulting+algorithm%252C+PCMCI%2524_%7bOmega%7d%2524%252C+can+capture+the+alternating+and+recurring+changes+in+the+causal+mechanisms+and+then+identify+the+underlying+causal+graph+with+conditional+independence+(CI)+tests.+We+show+that+this+algorithm+is+sound+in+identifying+causal+relations+on+discrete+time+series.+We+validate+the+algorithm+with+extensive+experiments+on+continuous+and+discrete+simulated+data.+We+also+apply+our+algorithm+to+a+real-world+climate+dataset.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.92534fe75307b2bd532c7cc845fc0045%3frik%3dOVFu7yfv8dmJLw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Causal+Discovery+Graph&FORM=IRPRST&ck=9AEBACA351A65B0A06372830091D83CB&selectedIndex=10&itb=0)
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