<div dir="ltr"><div><div><div>[1] Consider x(ntim), y(ntim) where ntim is the total number of days across all years . <br> eg: 10 years would yield 10*365 values (few extra for leap years). If your data are <br> not in this form ... put them in this form.<br><br></div><div>[2] Read about Fischer z-transform: <a href="http://en.wikipedia.org/wiki/Fisher_transformation" target="_blank">http://en.wikipedia.org/wiki/Fisher_transformation</a><br></div><br></div>[3] rfz = 0.0<br></div> nfz = 0<br><div> nDay = 92 ; season length<br><div><div><div><div><div><div> do ns=0,nSea-1 ; season or year<br> iStrt = iSea(ns) ; index values to select start of desired seasons<br> iLast = iStrt+nDay-1 <br> r = escorc(x(iStrt:iLast),y(iStrt:iLast),...) or esccr(...)<br> <br></div><div> nfz = nfz +1<br></div><div> rfz = rfz + 0.5*log((1+r)/(1-r)) ; Fischer z-transform <br> end do<br><br> rfz = rfz/nfz ; mean zf; test with this<br> rSea = (exp(2*rfz)-1)/(exp(2*rfz)+1) ; return to original 'space'<br><br></div></div></div></div></div></div></div></div><div class="gmail_extra"><br><div class="gmail_quote">On Sat, Oct 11, 2014 at 1:44 AM, Erika Folova <span dir="ltr"><<a href="mailto:e.folova@gmail.com" target="_blank">e.folova@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Hi Maria,<div><br></div><div>The second option, if i do that carefully for instance lag -1, by removing one day per each season each year, then combine them into one series in such a way avoiding the issue you mentioned. The problem that im still worried is about the transision of year, as it will treat your series as continous series, but actually it is discret, DJF-DJF where february meets december next year, what do you think? i think this issue also remains for lag 0.</div><div class="HOEnZb"><div class="h5"><div><br></div><div><br><br>On Saturday, October 11, 2014, Maria Gehne - NOAA Affiliate <<a href="mailto:maria.gehne@noaa.gov" target="_blank">maria.gehne@noaa.gov</a>> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Hi Erika,<div><br></div><div>I think it depends. Are you interested in cross-correlations at lag zero only, or do you want non-zero lags as well? </div><div><br></div><div>In the first case it doesn't make any difference what you do. Both approaches should give you the same answer. </div><div><br></div><div>In the second case you may be better off computing the cross-correlation for each season and each year and then averaging over all years. Otherwise (if you put all DJFs into one single time series) you may end up correlating dates from one year with the next year (at non-zero lags), which you don't want.</div><div><br></div><div>I hope this helps,</div><div>Maria</div></div><div class="gmail_extra"><br><div class="gmail_quote">On Mon, Oct 6, 2014 at 3:52 PM, Erika Folova <span dir="ltr"><<a>e.folova@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Hallo evryone,<div><br></div><div>I have daily data multiplyr years and I would like to perform the cross correlation of daily data in specific season lets say DJF.</div><div>What do you suggest, do i have to calculate the cross correlation of DJF each year then averaging them by number of N seasons? or</div><div>I just need to combine the time series DJF from different years to be one time series and perform the cross correlation?</div><div><br></div><div>Your insight would be really helpful.</div>Cheers<span></span>
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