SQL Fiddle select “date”, shop_id, amount, extract(dow from date), case when row_number() over (order by date) > 3 then avg(amount) OVER ( ORDER BY date DESC ROWS BETWEEN 1 following AND 3 FOLLOWING ) else null end from ( select * from ro where extract(dow from date) = 4 ) s What is wrong with … Read more
Try: select x.*, avg(dailyusage) over(partition by productid order by productid, date rows between 6 preceding and current row) as rolling_avg from (select productid, date, sum(usagecount) as dailyusage from tbl group by productid, date) x Fiddle: http://sqlfiddle.com/#!6/f674a7/4/0 Replace “avg(dailusage) over….” with sum (rather than avg) if what you really want is the sum for the past … Read more
You can use rolling with transform: df[‘moving’] = df.groupby(‘object’)[‘value’].transform(lambda x: x.rolling(10, 1).mean()) The 1 in rolling is for minimum number of periods.
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()
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
If you are trying to remove the occasional odd value, a low-pass filter is the best of the three options that you have identified. Low-pass filters allow low-speed changes such as the ones caused by rotating a compass by hand, while rejecting high-speed changes such as the ones caused by bumps on the road, for … 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
Your solution is essentially the “standard” optimal online solution for keeping a running track of average without storing big sums and also while running “online”, i.e. you can just process one number at a time without going back to other numbers, and you only use a constant amount of extra memory. If you want a … 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
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