Multi-Step LSTM Network/多步LSTM网络
In this section, we will use the persistence example as a starting point and look at the changes needed to fit an LSTM to the training data and make multi-step forecasts for the test dataset.
本节，我们将用持续性例子作为起点，看看需要做哪些改变来在训练数据上拟合LSTM,并且在测试数据集上做出多步预测。
Prepare Data/准备数据
The data must be prepared before we can use it to train an LSTM.
数据必须被准备（说处理应该更合适），在用其训练一个LSTM之前
Specifically, two additional changes are required:
具体来说，需要两个额外的更改。
- Stationary. The data shows an increasing trend that must be removed by differencing.
固化。数据显示一个增长趋势其必须用差分来消除。（解释的一点都不清楚） - Scale. The scale of the data must be reduced to values between -1 and 1, the activation function of the LSTM units.
比例。数据的比例必须被减小到-1~1之间的值，这是LSTM单元的激活函数（的值的取值范围）
We can introduce a function to make the data stationary called difference(). This will transform the series of values into a series of differences, a simpler representation to work with.
我们可以引入一个函数来使数据固化，称为差分（）。 这将把一个值的序列转化为一个差分的序列，一个更简单的表现形式。
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# create a differenced series
def
difference
(
dataset
,
interval
=
1
)
:
diff
=
list
(
)
for
i
in
range
(
interval
,
len
(
dataset
)
)
:
value
=
dataset
[
i
]
-
dataset
[
i
-
interval
]
diff
.
append
(
value
)
return
Series
(
diff
)
|
We can use the MinMaxScaler from the sklearn library to scale the data.
我们可以使用sklearn库中的MinMaxScaler来缩放数据。
Putting this together, we can update the prepare_data() function to first difference the data and rescale it, then perform the transform into a supervised learning problem and train test sets as we did before with the persistence example.
综合起来，我们可以更新prepare_data（）函数来首先差分数据并重缩放它，然后执行转换为监督学习问题，并像以前一样使用持续性示例来训练测试集。
The function now returns a scaler in addition to the train and test datasets.
除了训练和测试数据集函数现在还返回一个缩放器。
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# transform series into train and test sets for supervised learning
def
prepare_data
(
series
,
n_test
,
n_lag
,
n_seq
)
:
# extract raw values
raw_values
=
series
.
values
# transform data to be stationary
diff_series
=
difference
(
raw_values
,
1
)
diff_values
=
diff_series
.
values
diff_values
=
diff_values
.
reshape
(
len
(
diff_values
)
,
1
)
# rescale values to -1, 1
scaler
=
MinMaxScaler
(
feature_range
=
(
-
1
,
1
)
)
scaled_values
=
scaler
.
fit_transform
(
diff_values
)
scaled_values
=
scaled_values
.
reshape
(
len
(
scaled_values
)
,
1
)
# transform into supervised learning problem X, y
supervised
=
series_to_supervised
(
scaled_values
,
n_lag
,
n_seq
)
supervised_values
=
supervised
.
values
# split into train and test sets
train
,
test
=
supervised_values
[
0
:
-
n_test
]
,
supervised_values
[
-
n_test
:
]
return
scaler
,
train
,
test
|
We can call this function as follows:
我们可以调用这个函数如下：
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# prepare data
scaler
,
train
,
test
=
prepare_data
(
series
,
n_test
,
n_lag
,
n_seq
)
|