I am trying to build a ARIMA for anomaly detection. I need to find the moving average of the time series graph I am trying to use pandas 0.23 for this
import pandas as pd import numpy as np from statsmodels.tsa.stattools import adfuller import matplotlib.pylab as plt from matplotlib.pylab import rcParams rcParams['figure.figsize'] = 15, 6 dateparse = lambda dates: pd.datetime.strptime(dates, '%Y-%m') data = pd.read_csv('AirPassengers.csv', parse_dates=['Month'], index_col='Month',date_parser=dateparse) data.index ts = data['#Passengers'] ts.head(10) plt.plot(ts) ts_log = np.log(ts) plt.plot(ts_log) moving_avg = pd.rolling_mean(ts_log,12) # here is the error pd.rolling_mean plt.plot(ts_log) plt.plot(moving_avg, color='red')
error:Traceback (most recent call last): File "C:\Program Files\Python36\lastmainprogram.py", line 74, in moving_avg = pd.rolling_mean(ts_log,12) AttributeError: module 'pandas' has no attribute 'rolling_mean'
I believe need change:
moving_avg = pd.rolling_mean(ts_log,12)
to:
moving_avg = ts_log.rolling(12).mean()
because old pandas version code below pandas 0.18.0
Change:
moving_avg = pd.rolling_mean(ts_log,12)
to:
rolmean = pd.Series(timeseries).rolling(window=12).mean() rolstd = pd.Series(timeseries).rolling(window=12).std()
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