So I have a CSV file with two columns: date and price, but when I tried to use ARIMA on that time series I encountered this error:
ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
' ignored when e.g. forecasting.', ValueWarning)
so I found these two questions:
ValueWarning: No frequency information was provided, so inferred frequency MS will be used
https://stackoverflow.com/a/35860703
but when I tried to run the code in the example ( the 2nd link ) :
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
I also received the same ValueWarning, so I tried to change this line:
df.index = pd.DatetimeIndex(df.index)
to this:
df.index = pd.DatetimeIndex(df.index.values, freq=df.index.inferred_freq)
but then I get this error:
AttributeError: 'Index' object has no attribute 'inferred_freq'
You current index, as printed, is string index. You should convert it to DatetimeIndex
and pass a frequency by to_period
:
df.index = pd.DatetimeIndex(df.index).to_period('M')