python - Time series cross-validation using linear regression from scikit learn -


i'm using linear regression model scikit learn explanatory fit on time series:

from sklearn import linear_model import numpy np  x = np.array([np.random.random(100), np.random.random(100)]) y = np.array(np.random.random(100))  regressor = linear_model.linearregression() regressor.fit(x, y) y_hat = regressor.predict(x)

i want cross-validate the prediction. far know, can't use cross_val sklearn (like kfold) because break down results randomly, , need folds sequentially. example,

data_set = [1 2 3 4 5 6 7 8 9 10]  # first train set train = [1] # first test set test = [2 3 4 5 6 7 8 9 10] #fit, predict, evaluate  # train set train = [1 2] # test set test = [3 4 5 6 7 8 9 10] #fit, predict, evaluate  ...  # train set train = [1 2 3 4 5 6 7 8] # test set test = [9 10] #fit, predict, evaluate

is possible using sklearn?

you not need scikit kind of folding. slicing sufficient, like:

step = 1  in range(0, len(data_set), step):   train = dataset[:i]   test = dataset[i:]   # etc...

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