tag:blogger.com,1999:blog-6123948034208228569.post3218615097379637746..comments2009-08-18T11:04:34.255-07:00Comments on Marty Fuhry [A Python Summer of Code]: Frequency Conversion UFuncAnonymoushttp://www.blogger.com/profile/14805298701218537717noreply@blogger.comBlogger2125tag:blogger.com,1999:blog-6123948034208228569.post-81601678849699813142009-08-18T11:04:34.255-07:002009-08-18T11:04:34.255-07:00I noticed the "relation" parameter when ...I noticed the "relation" parameter when I was looking through TimeSeries. I was thinking about that and custom "start" dates for things like weeks (instead of making the default "Sunday". <br /><br />They're great ideas, and like you said, very helpful in some situations. I'll look into working them sometime down the road.Anonymoushttps://www.blogger.com/profile/14805298701218537717noreply@blogger.comtag:blogger.com,1999:blog-6123948034208228569.post-62698918571393127432009-08-16T11:19:23.834-07:002009-08-16T11:19:23.834-07:00is the frequency conversion function going to supp...is the frequency conversion function going to support a "relation" parameter like in the asfreq function in the scikits.timeseries package? This optional parameter is used when going from a lower frequency to a higher frequency (eg. monthly to daily) so you can specify if you want the result to be the first period or last period (eg. first day of month or last day of month).<br /><br />It may not be obvious at first what one would use this for, but my typical example would be when "comparing" a daily series like a stock price index to some general economic data like inflation or gdp. Rather than using the lowest frequency of the input series, one could use the highest frequency and then "forward fill" or somehow interpolate the lower frequency series after converting them.Matt Knox, CFAhttps://www.blogger.com/profile/09557527893933925202noreply@blogger.com