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Quick update: we have now removed downsampling for time series with less than 100K data points (before 10K).
I understand.Keep also in mind that the moving average function (as it is implemented today) computes the pointwise average in each window, e.g.(sum_x f(x)) / N,where N is the number of data points x with values f(x) in the window - and not the integral average, i.e.(integral t0..t1 f(x)) / (t1 - t0),where t0 and t1 are the start and endtime of the window.Is it the pointwise average that you are looking for? Regarding your question on automatic resampling: with automatic resampling, data points of two or more time series are automatically aligned to a common timestamps if needed (by interpolation). The simplest example is adding two time series with non-matching timestamps. Without automatic reindexing the sum output will be empty (because none of the timestamps match), while with automatic reindexing, the sum output will have a value at the timestamps of both inputs. Finally: I am working on reducing the amount of downsampling that we do by a significant amount (hopefully 10x). We ar
I think there might be a misunderstanding about the sample parameter. The simple moving average has a “Minimum sample” parameter - if a window has < this number of data points, the result of this window will be “Not a Number” (which in practice means that the result for this window is not shown in CHARTS).In other words: setting “Minimum samples” to a high value means that you will see fewer results (and maybe no results at all depending on the window size and the density of the data points).
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