sklearn.model_selection.TimeSeriesSplit

Time Series cross-validator

Provides train/test indices to split time series data samples
that are observed at fixed time intervals, in train/test sets.
In each split, test indices must be higher than before, and thus shuffling
in cross validator is inappropriate.

This cross-validation object is a variation of KFold.
In the kth split, it returns first k folds as train set and the
(k+1)th fold as test set.

Note that unlike standard cross-validation methods, successive
training sets are supersets of those that come before them.

Read more in the User Guide.

New in version 0.18.

Parameters

:

n_splits

int, default=5

Number of splits. Must be at least 2.

Changed in version 0.22: n_splits default value changed from 3 to 5.

max_train_size

int, default=None

Maximum size for a single training set.

test_size

int, default=None

Used to limit the size of the test set. Defaults to
n_samples // (n_splits + 1), which is the maximum allowed value
with gap=0.

New in version 0.24.

gap

int, default=0

Number of samples to exclude from the end of each train set before
the test set.

New in version 0.24.

Notes

The training set has size i * n_samples // (n_splits + 1)
+ n_samples % (n_splits + 1) in the i th split,
with a test set of size n_samples//(n_splits + 1) by default,
where n_samples is the number of samples.

Examples

>>> 
import
 
numpy
 
as
 
np
>>> 
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
()
>>> 
print
(
tscv
)
TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None)
>>> 
for
 
i
,
 
(
train_index
,
 
test_index
)
 
in
 
enumerate
(
tscv
.
split
(
X
)):
... 
    
print
(
f
"Fold 
{
i
}
:"
)
... 
    
print
(
f
"  Train: index=
{
train_index
}
"
)
... 
    
print
(
f
"  Test:  index=
{
test_index
}
"
)
Fold 0:
  Train: index=[0]
  Test:  index=[1]
Fold 1:
  Train: index=[0 1]
  Test:  index=[2]
Fold 2:
  Train: index=[0 1 2]
  Test:  index=[3]
Fold 3:
  Train: index=[0 1 2 3]
  Test:  index=[4]
Fold 4:
  Train: index=[0 1 2 3 4]
  Test:  index=[5]
>>> 
# Fix test_size to 2 with 12 samples
>>> 
X
 
=
 
np
.
random
.
randn
(
12
,
 
2
)
>>> 
y
 
=
 
np
.
random
.
randint
(
0
,
 
2
,
 
12
)
>>> 
tscv
 
=
 
TimeSeriesSplit
(
n_splits
=
3
,
 
test_size
=
2
)
>>> 
for
 
i
,
 
(
train_index
,
 
test_index
)
 
in
 
enumerate
(
tscv
.
split
(
X
)):
... 
    
print
(
f
"Fold 
{
i
}
:"
)
... 
    
print
(
f
"  Train: index=
{
train_index
}
"
)
... 
    
print
(
f
"  Test:  index=
{
test_index
}
"
)
Fold 0:
  Train: index=[0 1 2 3 4 5]
  Test:  index=[6 7]
Fold 1:
  Train: index=[0 1 2 3 4 5 6 7]
  Test:  index=[8 9]
Fold 2:
  Train: index=[0 1 2 3 4 5 6 7 8 9]
  Test:  index=[10 11]
>>> 
# Add in a 2 period gap
>>> 
tscv
 
=
 
TimeSeriesSplit
(
n_splits
=
3
,
 
test_size
=
2
,
 
gap
=
2
)
>>> 
for
 
i
,
 
(
train_index
,
 
test_index
)
 
in
 
enumerate
(
tscv
.
split
(
X
)):
... 
    
print
(
f
"Fold 
{
i
}
:"
)
... 
    
print
(
f
"  Train: index=
{
train_index
}
"
)
... 
    
print
(
f
"  Test:  index=
{
test_index
}
"
)
Fold 0:
  Train: index=[0 1 2 3]
  Test:  index=[6 7]
Fold 1:
  Train: index=[0 1 2 3 4 5]
  Test:  index=[8 9]
Fold 2:
  Train: index=[0 1 2 3 4 5 6 7]
  Test:  index=[10 11]

Methods

get_n_splits([X, y, groups])

Returns the number of splitting iterations in the cross-validator

split(X[, y, groups])

Generate indices to split data into training and test set.

get_n_splits

(

X

=

None

,

y

=

None

,

groups

=

None

)

[source]

¶

Returns the number of splitting iterations in the cross-validator

Parameters

:

X

object

Always ignored, exists for compatibility.

y

object

Always ignored, exists for compatibility.

groups

object

Always ignored, exists for compatibility.

Returns

:

n_splits

int

Returns the number of splitting iterations in the cross-validator.

split

(

X

,

y

=

None

,

groups

=

None

)

[source]

¶

Generate indices to split data into training and test set.

Parameters

:

X

array-like of shape (n_samples, n_features)

Training data, where n_samples is the number of samples
and n_features is the number of features.

y

array-like of shape (n_samples,)

Always ignored, exists for compatibility.

groups

array-like of shape (n_samples,)

Always ignored, exists for compatibility.

Yields

:

train

ndarray

The training set indices for that split.

test

ndarray

The testing set indices for that split.