scikit-learn cross validation custom splits for time series data

Question

I'd like to use scikit-learn's GridSearchCV to determine some hyper parameters for a random forest model. My data is time dependent and looks something like

import pandas as pd

train = pd.DataFrame({'date': pd.DatetimeIndex(['2012-1-1', '2012-9-30', '2013-4-3', '2014-8-16', '2015-3-20', '2015-6-30']), 
'feature1': [1.2, 3.3, 2.7, 4.0, 8.2, 6.5],
'feature2': [4, 4, 10, 3, 10, 9],
'target': [1,2,1,3,2,2]})

>>> train
        date  feature1  feature2  target
0 2012-01-01       1.2         4       1
1 2012-09-30       3.3         4       2
2 2013-04-03       2.7        10       1
3 2014-08-16       4.0         3       3
4 2015-03-20       8.2        10       2
5 2015-06-30       6.5         9       2

How can I implement the following cross validation folding technique?

train:(2012, 2013) - test:(2014)
train:(2013, 2014) - test:(2015)

That is, I want to use 2 years of historic observations to train a model and then test its accuracy in the subsequent year.

Answer 1

There is also the TimeSeriesSplit function in sklearn, which splits time-series data (i.e. with fixed time intervals), in train/test sets. Note that unlike standard cross-validation methods, successive training sets are supersets of those that come before them, i.e. in each split, test indices must be higher than before, and thus shuffling in cross validator is inappropriate.