The window functions in SQL 2008 are rather limited compared to later versions and if I remember correct you can only partition and you can’t use any rows/range frame limit but I think this might be what you want: ;WITH cte (rn, transactionid, value) AS ( SELECT rn = ROW_NUMBER() OVER (ORDER BY transactionid), transactionid, … Read more
There are several ways to initialize an exponential moving average, so I wouldn’t say pandas is doing it wrong, just different. Here would be a way to calculate it like you want: In [20]: s.head() Out[20]: 0 22.27 1 22.19 2 22.08 3 22.17 4 22.18 Name: Price, dtype: float64 In [21]: span = 10 … Read more
This is a similar concept to applying a filter to an image. Fortunately, scipy.ndimage.filters has a bunch of functions to do that. The one you’re after is scipy.ndimage.uniform_filter. Can be used like this: a => array([[ 0., 1., 2., 3., 4.], [ 5., 6., 7., 8., 9.], [ 10., 11., 12., 13., 14.], [ 15., … Read more
I figured out the correct way to calculate a moving/rolling average using this stackoverflow: Spark Window Functions – rangeBetween dates The basic idea is to convert your timestamp column to seconds, and then you can use the rangeBetween function in the pyspark.sql.Window class to include the correct rows in your window. Here’s the solved example: … Read more
Convolution is a mathematical operator primarily used in signal processing. Numpy simply uses this signal processing nomenclature to define it, hence the “signal” references. An array in numpy is a signal. The convolution of two signals is defined as the integral of the first signal, reversed, sweeping over (“convolved onto”) the second signal and multiplied … Read more
You can simply do: double approxRollingAverage (double avg, double new_sample) { avg -= avg / N; avg += new_sample / N; return avg; } Where N is the number of samples where you want to average over. Note that this approximation is equivalent to an exponential moving average. See: Calculate rolling / moving average in … Read more
If your needs are simple, you might just try using an exponential moving average. http://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average Put simply, you make an accumulator variable, and as your code looks at each sample, the code updates the accumulator with the new value. You pick a constant “alpha” that is between 0 and 1, and compute this: accumulator = … Read more
You can use rollmean, but set align=’right’. Or you could use rollmeanr, which has align=’right’ as the default. ma3 <- rollmeanr(x[,1],3,fill=NA) …but you would still need to lag the result. Another solution is to use rollapply with a list for the width argument: ma3 <- rollapplyr(x[,1],list(-(3:1)),mean,fill=NA)
You can use the sliding function from MLLIB which probably does the same thing as Daniel’s answer. You will have to sort the data by time before using the sliding function. import org.apache.spark.mllib.rdd.RDDFunctions._ sc.parallelize(1 to 100, 10) .sliding(3) .map(curSlice => (curSlice.sum / curSlice.size)) .collect()
The rolling mean returns a Series you only have to add it as a new column of your DataFrame (MA) as described below. For information, the rolling_mean function has been deprecated in pandas newer versions. I have used the new method in my example, see below a quote from the pandas documentation. Warning Prior to … Read more