I am trying to fit an ARMA model to a time series stored in a pandas dataframe. The dataframe has one column of values of type numpy.float64 named "val" and an index of pandas timestamps. The timestamps are in the "Year-Month-Day Hour:Minute:Second" format. I understand that the following code:
from statsmodels.tsa.arima_model import ARMA
model = ARMA(df["val"], (1,0))
gives me the error message:
ValueError: Given a pandas object and the index does not contain dates
because I have not formatted the timestamps correctly. How can I index my dataframe so that the ARMA method accepts it while retaining my date and time information?
I think you need convert index
to DatetimeIndex
:
df.index = pd.DatetimeIndex(df.index)
Sample:
import pandas as pd
from statsmodels.tsa.arima_model import ARMA
df=pd.DataFrame({"val": pd.Series([1.1,1.7,8.4 ],
index=['2015-01-15 12:10:23','2015-02-15 12:10:23','2015-03-15 12:10:23'])})
print df
val
2015-01-15 12:10:23 1.1
2015-02-15 12:10:23 1.7
2015-03-15 12:10:23 8.4
print df.index
Index([u'2015-01-15 12:10:23',u'2015-02-15 12:10:23',u'2015-03-15 12:10:23'], dtype='object')
df.index = pd.DatetimeIndex(df.index)
print df.index
DatetimeIndex(['2015-01-15 12:10:23', '2015-02-15 12:10:23',
'2015-03-15 12:10:23'],
dtype='datetime64[ns]', freq=None)
model = ARMA(df["val"], (1,0))
print model
<statsmodels.tsa.arima_model.ARMA object at 0x000000000D5247B8>
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