moving-average | 易学教程

Calculate rolling / moving average in C++

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问题 I know this is achievable with boost as per: Using boost::accumulators, how can I reset a rolling window size, does it keep extra history? But I really would like to avoid using boost. I have googled and not found any suitable or readable examples. Basically I want to track the moving average of an ongoing stream of a stream of floating point numbers using the most recent 1000 numbers as a data sample. What is the easiest way to achieve this? I experimented with using a circular array,

Moving average of previous three values in R

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In the zoo package there is a function called rollmean, which enables you to make moving averages. The rollmean(x,3) will take the previous, current and next value (ie 4, 6 and 2) in the table below. This is shown in the second column. x rollmean ma3 4 6 4.0 2 4.3 5 3.0 4.0 2 6.3 4.3 12 6.0 3.0 4 6.0 6.3 2 6.0 I would like to get the same job done, but by averaging out the previous 3 values in the fourth row. This is displayed in the third column. Can anybody tell me the name of the function that will help to accomplish this? w_i_l_l I struggled searching for a simple function for moving

applying rolling mean by group in R

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问题 I'm an R newbie and I'm having a lot of trouble doing something that is probably very simple. I have a big dataset split up into groups by country code, and I want to take a 3-month rolling average of a price index, by country, and then put it into a new column that matches up to the appropriate month. I've been trying to use rollmean like this with no success (code and error messages below): > leader$last3<-tapply(leader, leader$ccode, function(x) rollmean(leader$GI_delta, 3, na.pad=T))

Moving Average based on Timestamps in PostgreSQL

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问题 I wanted to perform moving average through timestamps. I have two columns: Temperature and timestamps (time-date) and I want to perform the moving average based on every 15 minutes successive temperature observations. In other words, selecting data to perform the average based on 15 minutes time interval. Moreover, it is possible to have different number of observations for different time sequences. I meant all the window sizes are equal (15 minutes) but it is possible to have different

Moving averages with MongoDB's aggregation framework?

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问题 If you have 50 years of temperature weather data (daily) (for example) how would you calculate moving averages, using 3-month intervals, for that time period? Can you do that with one query or would you have to have multiple queries? Example Data 01/01/2014 = 40 degrees 12/31/2013 = 38 degrees 12/30/2013 = 29 degrees 12/29/2013 = 31 degrees 12/28/2013 = 34 degrees 12/27/2013 = 36 degrees 12/26/2013 = 38 degrees ..... 回答1: The agg framework now has $map and $reduce and $range built in so array

How to calculate moving average without keeping the count and data-total?

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问题 I am trying to find a way to calculate a moving cumulative average without storing the count and total data that is received so far. I came up with two algorithms but both need to store the count: new average = ((old count * old data) + next data) / next count new average = old average + (next data - old average) / next count The problem with these methods is that the count gets bigger and bigger resulting in losing precision in the resulting average. The first method uses the old count and

Numpy Two-Dimensional Moving Average

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问题 I have a 2d numpy array. I want to take the average value of the n nearest entries to each entry, just like taking a sliding average over a one-dimensional array. What is the cleanest way to do this? 回答1: 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

T-SQL calculate moving average

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问题 I am working with SQL Server 2008 R2, trying to calculate a moving average. For each record in my view, I would like to collect the values of the 250 previous records, and then calculate the average for this selection. My view columns are as follows: TransactionID | TimeStamp | Value | MovAvg ---------------------------------------------------- 1 | 01.09.2014 10:00:12 | 5 | 2 | 01.09.2014 10:05:34 | 3 | ... 300 | 03.09.2014 09:00:23 | 4 | TransactionID is unique. For each TransactionID , I

Moving Average- Pandas

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问题 I would like to add a moving average calculation to my exchange time series. Original data from Quandl Exchange = Quandl.get("BUNDESBANK/BBEX3_D_SEK_USD_CA_AC_000", authtoken="xxxxxxx") Value Date 1989-01-02 6.10500 1989-01-03 6.07500 1989-01-04 6.10750 1989-01-05 6.15250 1989-01-09 6.25500 1989-01-10 6.24250 1989-01-11 6.26250 1989-01-12 6.23250 1989-01-13 6.27750 1989-01-16 6.31250 Calculating Moving Avarage MovingAverage = pd.rolling_mean(Exchange,5) Value Date 1989-01-02 NaN 1989-01-03

Moving average of previous three values in R

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问题 In the zoo package there is a function called rollmean, which enables you to make moving averages. The rollmean(x,3) will take the previous, current and next value (ie 4, 6 and 2) in the table below. This is shown in the second column. x rollmean ma3 4 6 4.0 2 4.3 5 3.0 4.0 2 6.3 4.3 12 6.0 3.0 4 6.0 6.3 2 6.0 I would like to get the same job done, but by averaging out the previous 3 values in the fourth row. This is displayed in the third column. Can anybody tell me the name of the function