moving-average

I would like to use the zoo function rollapply to apply a function (for example mean) on a time series but only using the last N known points. For example: x = zoo(c(1,2,3,4), order.by=c(10,11,12,13)) rollmean(x,2) Produces: 10 11 12 1.5 2.5 3.5 I would like to produce a series that would have date entries of 11, 12, 13 and values of 1.5, 2.5, 3.5. The values seem correct but the dates that rollmean outputs don't seem to correspond to what I would like. I'm a bit worried about just assigning the dates I want to the zoo object using time(x)<- because I'm not sure that rollapply is actually

I would like to get rolling average for each of the numeric variables that I have. Using data.table package, I know how to compute for a single variable. But how should I revise the code so it can process multiple variables at a time rather than revising the variable name and repeat this procedure for several times? Thanks. Suppose I have other numeric variables named as "V2", "V3", and "V4". require(data.table) setDT(data) setkey(data,Receptor,date) data[ , `:=` ('RollConc' = rollmean(AvgConc, 48, align="left", na.pad=TRUE)) , by=Receptor] A copy of my sample data can be found at: https:/

I'd like to compare each value x of an array with a rolling window of the n previous values. More precisely I'd like to see at which percentile this new value x would be, if we added it to the previous window : import numpy as np A = np.array([1, 4, 9, 28, 28.5, 2, 283, 3.2, 7, 15]) print A n = 4 # window width for i in range(len(A)-n): W = A[i:i+n] x = A[i+n] q = sum(W <= x) * 1.0 / n print 'Value:', x, ' Window before this value:', W, ' Quantile:', q [ 1. 4. 9. 28. 28.5 2. 283. 3.2 7. 15. ] Value: 28.5 Window before this value: [ 1. 4. 9. 28.] Quantile: 1.0 Value: 2.0 Window before this

I am new to R and having trouble figuring out to go about this. I have data on tree growth rates from dead trees, organized by year. So, my first column is year and the columns to the right are growth rates for individual trees, ending in the year each tree died. After the tree died, the values are "NA" for the remaining years in the dataset. I need to take the mean growth for the 10 years preceding each tree's death, but each tree died in a different year. Does anyone have an idea for how to do this? Here is an example of what a dataset might look like: Year Tree1 Tree2 Tree3 1989 53.00 84.58

I have a dataset for price of the ticker on the stock exchange: time - price. But intervals between data points are not equal - from 1 to 2 minutes. What is the best practice to calculate moving average for such case? How to make it in Matlab? I tend to think, that weights of the points should depend on the time interval that was last since previous point. Does we have function in Matlab to calculate moving average with custom weights of the points? Here is an example of the "naive" approach I mentioned in the comments above: % some data (unequally spaced in time, but monotonically non