I have a pandas data-frame like this:(index) 0           sie0       1997-01-01  11.21        1997-01-03  12.32        1997-01-04  11.5...12454    2017-02-01  13.2I want to use SVR to predict future values of the sie. How can I implement a python code to predict values?I am doing something like this model = svm.SVR().fit(df[0],df['sie'])but giving me: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 a continuous data(some dates,in between, are missing), also values in 0 are datetime.date() objects.
Time series forecast using SVM?
python;predictive modeling;time series;svm
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. 