# Time series forecast using SVM?

I have a pandas data frame like this:

(index) 0           sie

0       1997-01-01  11.2
1        1997-01-03  12.3
2        1997-01-04  11.5
...
12454    2017-02-01  13.2


I would like to use SVM to predict the future values of the sie. How can I implement python code to predict these values?

I am doing something like this:

model = svm.SVR().fit(df[0],df['sie'])

But it is giving me this error:

ValueError: Found input variables with inconsistent numbers of samples: [1, 12455]

Although both df[0]anddf['sie'] have same shape of (12455,)

Note: I don't have continuous data (some dates, in between, are missing), also values in 0 are datetime.date() objects.

• Try this model = svm.SVR().fit(df['0'],df['sie']) – Grasshopper Jun 14 '17 at 12:49
• Giving KeyError: '0'. I don't think this is a problem though.. – vizakshat Jun 14 '17 at 12:52
• I used df.rename(columns={0:'Dates'}, inplace=True) and model = svm.SVR().fit(df['Dates'],df['sie']) still giving me **ValueError** – vizakshat Jun 14 '17 at 12:59

I used to solve the value error: model = svm.SVR().fit(np.transpose(np.matrix(df['Dates'])),np.transpose(np.matrix(df['sie'])))