moving-average

问题 I have a dataframe containing time series for 100 objects: object period value 1 1 24 1 2 67 ... 1 1000 56 2 1 59 2 2 46 ... 2 1000 64 3 1 54 ... 100 1 451 100 2 153 ... 100 1000 21 I want to calculate moving average with window 10 for the value column. I guess I have to do something like df.groupby('object').apply(lambda ~calculate MA~) and then merge this Series to the original dataframe by object? Can't figure out exact commands 回答1: You can use rolling with transform : df['moving'] = df

问题 The problem Tensorflow includes the function tf.train.ExponentialMovingAverage which allows us to apply a moving average to the parameters, which I've found to be great to stabilize the testing of the model. With that said, I've found it somewhat irritatingly hard to apply this to general models. My so far most successful approach (shown below) has been to write a function decorator and then put my whole NN inside a function. This does however have several downsides. For one, it duplicates

问题 When calculating a simple moving average, numpy.convolve appears to do the job. Question: How is the calculation done when you use np.convolve(values, weights, 'valid') ? When the docs mentioned convolution product is only given for points where the signals overlap completely , what are the 2 signals referring to? If any explanations can include examples and illustrations, it will be extremely useful. window = 10 weights = np.repeat(1.0, window)/window smas = np.convolve(values, weights,

问题 Trying to get the moving average on a per X months basis using SQLite. Problem is i cannot seem to figure or find anything remotely useful on how to aggregate with a X month(s) lookback period and thus create a moving average. Table CREATE TABLE "nav" ( `id` TEXT, `nav` NUMERIC, `date` TEXT ); Sample data id nav date 1380 15.3 2005-01-09 1380 15.4 2005-01-16 1380 15.5 2005-01-23 1380 15.55 2005-01-30 1380 15.66 2005-02-06 1380 15.45 2005-02-13 1380 15.26 2005-02-20 1380 15.14 2005-02-27 1380

问题 I have a table [production] that contains the following structure: rep (char(10)) ,cyc_date (datetime) ---- already standardized to mm/01/yyyy ,amt (decimal) I have data for each rep from 1/1/2011 to 8/1/2013. What I want to be able to do is create a 12 month moving average beginning 1/1/2012 for each rep, as follows: rep cyc_dt 12moAvg ------------------------- A 1/1/2012 10000.01 A 2/1/2012 13510.05 . ........ ........ A 8/1/2013 22101.32 B 1/1/2012 98328.22 B ........ ........ where each

问题 I'm new to Spark and need to do an assignment in which I will predict the stock price direction using Random Forests. To do this I need to calculate certain features like Moving Average. I already read in my data (100 csv files with 6 columns: time, open, close, high, low, volume) using wholeTextFiles. So now I have an RDD of file names and content. What is the most efficient way to transform this RDD in order to be able to calculate the moving average of the close column? Should I make an

问题 Generally in a packet pair estimation, you are supposed to send multiple bursts of packet pairs and take an average of the bandwidths. Say, you send 4 packets, calculate the time difference between the first two packets(1&2), time difference between the next two packets(3&4). Calculate bandwidth for each of these two and then take average. Or Calculate the time difference between (1&2), time difference between (3&2), time difference between (4&3)... and then take an average out of these

问题 Generally in a packet pair estimation, you are supposed to send multiple bursts of packet pairs and take an average of the bandwidths. Say, you send 4 packets, calculate the time difference between the first two packets(1&2), time difference between the next two packets(3&4). Calculate bandwidth for each of these two and then take average. Or Calculate the time difference between (1&2), time difference between (3&2), time difference between (4&3)... and then take an average out of these

问题 The Pandas documentation http://pandas.pydata.org/pandas-docs/stable/computation.html has an example of how to calculate moving averages: ser = pd.Series(np.random.randn(10), index=pd.date_range('1/1/2000', periods=10)) pd.rolling_window(ser, 5, 'boxcar') The second line calculates a rolling average with a window of 5 and equal weights on each of the five observations. The docs refer tantalizingly to the possibility of using custom weights ("When passing a win_type instead of explicitly

问题 The Pandas documentation http://pandas.pydata.org/pandas-docs/stable/computation.html has an example of how to calculate moving averages: ser = pd.Series(np.random.randn(10), index=pd.date_range('1/1/2000', periods=10)) pd.rolling_window(ser, 5, 'boxcar') The second line calculates a rolling average with a window of 5 and equal weights on each of the five observations. The docs refer tantalizingly to the possibility of using custom weights ("When passing a win_type instead of explicitly