Timeseries is an awesome package. Great contribution. I have 2 questions about it, though. 1. Is scipy-user the right place for questions? 2. I've noticed that 'business frequency' includes holidays, and that can create holes in what are actually complete data sets. For instance, Sep 01, 2008 was a holiday in the US (Labor Day). However, it is included in a DateArray spanning that date. For instance. In [640]: ts.date_array(ts.Date('B','2008-08-25'), length=12) Out[640]: DateArray([25-Aug-2008, 26-Aug-2008, 27-Aug-2008, 28-Aug-2008, 29- Aug-2008, 01-Sep-2008, 02-Sep-2008, 03-Sep-2008, 04-Sep-2008, 05-Sep-2008, 08-Sep-2008, 09-Sep-2008], freq='B') This makes stock ticker data look like it's incomplete - no data for Sep 01, since the markets were closed. For instance, if I use matplotlib.finance.quotes_historical_yahoo to download Intel data, and put that into the date array above, I get the series: masked_array(data = [22.77 22.95 23.21 23.39 22.67 -- 22.39 21.35 20.34 20.43 20.79], mask = [False False False False False True False False False False False], fill_value=1e+20) That has a hole on Sep 1. This matters for things like moving average calculation. Sep 1 should be treated like a Saturday or Sunday, but instead causes a 5-day mov_average calculation to not compute anything from Sep 2 through Sep 7: timeseries([-- -- -- -- 22.998 -- -- -- -- -- 21.06], dates = [25-Aug-2008 ... 08-Sep-2008], freq = B) My question: What is a good way to handle (get rid of?) the holes in the series? thanks, -robert