moving-average

If you have 50 years of temperature weather data (daily) (for example) how would you calculate moving averages, using 3-month intervals, for that time period? Can you do that with one query or would you have to have multiple queries? Example Data 01/01/2014 = 40 degrees 12/31/2013 = 38 degrees 12/30/2013 = 29 degrees 12/29/2013 = 31 degrees 12/28/2013 = 34 degrees 12/27/2013 = 36 degrees 12/26/2013 = 38 degrees ..... The agg framework now has $map and $reduce and $range built in so array processing is much more straightfoward. Below is an example of calculating moving average on a set of data

I have a plot of time series in ggplot2 package and I have performed the Moving average and I would like to add the result of moving average to the plot of time series. Sample of Data-set (p31): ambtemp dt -1.14 2007-09-29 00:01:57 -1.12 2007-09-29 00:03:57 -1.33 2007-09-29 00:05:57 -1.44 2007-09-29 00:07:57 -1.54 2007-09-29 00:09:57 -1.29 2007-09-29 00:11:57 Applied code for time series presentation: Require(ggplot2) library(scales) p29$dt=strptime(p31$dt, "%Y-%m-%d %H:%M:%S") ggplot(p29, aes(dt, ambtemp)) + geom_line() + scale_x_datetime(breaks = date_breaks("2 hour"),labels=date_format("%H:

I would like to add a moving average calculation to my exchange time series. Original data from Quandl Exchange = Quandl.get("BUNDESBANK/BBEX3_D_SEK_USD_CA_AC_000", authtoken="xxxxxxx") Value Date 1989-01-02 6.10500 1989-01-03 6.07500 1989-01-04 6.10750 1989-01-05 6.15250 1989-01-09 6.25500 1989-01-10 6.24250 1989-01-11 6.26250 1989-01-12 6.23250 1989-01-13 6.27750 1989-01-16 6.31250 Calculating Moving Avarage MovingAverage = pd.rolling_mean(Exchange,5) Value Date 1989-01-02 NaN 1989-01-03 NaN 1989-01-04 NaN 1989-01-05 NaN 1989-01-09 6.13900 1989-01-10 6.16650 1989-01-11 6.20400 1989-01-12 6

I am working with SQL Server 2008 R2, trying to calculate a moving average. For each record in my view, I would like to collect the values of the 250 previous records, and then calculate the average for this selection. My view columns are as follows: TransactionID | TimeStamp | Value | MovAvg ---------------------------------------------------- 1 | 01.09.2014 10:00:12 | 5 | 2 | 01.09.2014 10:05:34 | 3 | ... 300 | 03.09.2014 09:00:23 | 4 | TransactionID is unique. For each TransactionID , I would like to calculate the average for column value, over previous 250 records. So for TransactionID 300

When trying to calculate the exponential moving average (EMA) from financial data in a dataframe it seems that Pandas' ewm approach is incorrect. The basics are well explained in the following link: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages When going to Pandas explanation, the approach taken is as follows (using the "adjust" parameter as False): weighted_average[0] = arg[0]; weighted_average[i] = (1-alpha) * weighted_average[i-1] + alpha * arg[i] This in my view is incorrect. The "arg" should be (for example) the closing values, however, arg[0