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.

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

Here, a very good article: http://machinelearningmastery.com/time-series-forecasting-supervised-learning/

In a few words, define a window of size n and that is the size of your feature vector. Reshape the dataset and play.

  • $\begingroup$ Actually I did not used sliding window method and trained my model. That was a disastrous mistake. The model trained well for the training time with X as the time feature :-P but predicted kinda average value for future times. Sliding window actually converts the time series into a supervised learning problem. \Thanks. $\endgroup$ – vizakshat Jun 15 '17 at 7:57

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

More Info: https://stackoverflow.com/questions/30813044/sklearn-found-arrays-with-inconsistent-numbers-of-samples-when-calling-linearre


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