sklearn: User defined cross validation for time series data

Question

I\'m trying to solve a machine learning problem. I have a specific dataset with time-series element. For this problem I\'m using well-known python library - sklea

Answer 1

Meanwhile this was added to the library: http://scikit-learn.org/stable/modules/cross_validation.html#time-series-split

Example from the doc:

>>> from sklearn.model_selection import TimeSeriesSplit

>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([1, 2, 3, 4, 5, 6])
>>> tscv = TimeSeriesSplit(n_splits=3)
>>> print(tscv)  
TimeSeriesSplit(n_splits=3)
>>> for train, test in tscv.split(X):
...     print("%s %s" % (train, test))
[0 1 2] [3]
[0 1 2 3] [4]
[0 1 2 3 4] [5]