moving-average

问题 I am trying to implement a vectorized exponentially weighted moving standard deviation using R. Is this the correct approach? ewma <- function (x, alpha) { c(stats::filter(x * ratio, 1 - ratio, "recursive", init = x[1])) } ewmsd <- function(x, alpha) { sqerror <- na.omit((x - lag(ewma(x, ratio)))^2) ewmvar <- c(stats::filter(sqerror * ratio, 1 - ratio, "recursive", init = 0)) c(NA, sqrt(ewmvar)) } I'm guessing it's not, since its output is different from Python's pandas.Series.ewm.std()

问题 I am trying to implement a vectorized exponentially weighted moving standard deviation using R. Is this the correct approach? ewma <- function (x, alpha) { c(stats::filter(x * ratio, 1 - ratio, "recursive", init = x[1])) } ewmsd <- function(x, alpha) { sqerror <- na.omit((x - lag(ewma(x, ratio)))^2) ewmvar <- c(stats::filter(sqerror * ratio, 1 - ratio, "recursive", init = 0)) c(NA, sqrt(ewmvar)) } I'm guessing it's not, since its output is different from Python's pandas.Series.ewm.std()

问题 Given a pandas dataframe in the following format: toy = pd.DataFrame({ 'id': [1,2,3, 1,2,3, 1,2,3], 'date': ['2015-05-13', '2015-05-13', '2015-05-13', '2016-02-12', '2016-02-12', '2016-02-12', '2018-07-23', '2018-07-23', '2018-07-23'], 'my_metric': [395, 634, 165, 144, 305, 293, 23, 395, 242] }) # Make sure 'date' has datetime format toy.date = pd.to_datetime(toy.date) The my_metric column contains some (random) metric I wish to compute a time-dependent moving average of, conditional on the

问题 I need to calculate some rolling forward averages in a dataframe and really don't know where to start. I know if I wanted to select a cell 10 days ahead say I would do df.shift(-10) , but what I'm looking to do is calculate the average between 10 and 15 days ahead say. So what I'm kind of thinking is df.rolling(-10,-15).mean() , if I was trying to calculate just a moving average going backing in time df.rolling(15, 10).mean() would work perfectly and I did think about just calculating the

问题 I have a dataframe with 2 columns - Date and Price. The data is sorted with newest date first (23 Jan in first row, 22 Jan in second row and so on). Date Price 23 Jan 100 22 Jan 95 21 Jan 90 . . . I want to calculate 2 days rolling average price for this time series data. I am using this: df.rolling(2).mean() What this does is, it assigns NaN to the first row (23 Jan) and then for the second row gives the output as the mean of prices on 23 Jan and 22 Jan. This is not useful as 22 Jan average

问题 I have a dataframe with 2 columns - Date and Price. The data is sorted with newest date first (23 Jan in first row, 22 Jan in second row and so on). Date Price 23 Jan 100 22 Jan 95 21 Jan 90 . . . I want to calculate 2 days rolling average price for this time series data. I am using this: df.rolling(2).mean() What this does is, it assigns NaN to the first row (23 Jan) and then for the second row gives the output as the mean of prices on 23 Jan and 22 Jan. This is not useful as 22 Jan average

问题 We can find the rolling/moving average of a time series data using window function in pyspark. The data I am dealing with doesn't have any timestamp column but it does have a strictly increasing column frame_number . Data looks like this. d = [ {'session_id': 1, 'frame_number': 1, 'rtd': 11.0, 'rtd2': 11.0,}, {'session_id': 1, 'frame_number': 2, 'rtd': 12.0, 'rtd2': 6.0}, {'session_id': 1, 'frame_number': 3, 'rtd': 4.0, 'rtd2': 233.0}, {'session_id': 1, 'frame_number': 4, 'rtd': 110.0, 'rtd2'

问题 We can find the rolling/moving average of a time series data using window function in pyspark. The data I am dealing with doesn't have any timestamp column but it does have a strictly increasing column frame_number . Data looks like this. d = [ {'session_id': 1, 'frame_number': 1, 'rtd': 11.0, 'rtd2': 11.0,}, {'session_id': 1, 'frame_number': 2, 'rtd': 12.0, 'rtd2': 6.0}, {'session_id': 1, 'frame_number': 3, 'rtd': 4.0, 'rtd2': 233.0}, {'session_id': 1, 'frame_number': 4, 'rtd': 110.0, 'rtd2'

来源： https://stackoverflow.com/questions/52456267/how-to-do-a-reverse-moving-average-in-pandas-rolling-mean-operation-on-pr

来源： https://stackoverflow.com/questions/52456267/how-to-do-a-reverse-moving-average-in-pandas-rolling-mean-operation-on-pr