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愉快的学习就从翻译开始吧_Multi-step Time Series Forecasting_10_Multi-Step LSTM Network_Make LSTM Forecasts

来源：星星旅游

Make LSTM Forecasts/让LSTM预测

The next step is to use the fit LSTM network to make forecasts.

下一步是用拟合的LSTM网络来做预测

A single forecast can be made with the fit LSTM network by calling model.predict(). Again, the data must be formatted into a 3D array with the format [samples, timesteps, features].

通过调用model.predict()，拟合的LSTM网络可以做单个预测，再次，数据必须被格式化为【samples，timesteps，features】的3D数组。

We can wrap this up into a function called forecast_lstm().

我们可以把这打包到一个名为forecast_lstm()的函数。

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# make one forecast with an LSTM,

def 
forecast_lstm
(
model
,
 
X
,
 
n_batch
)
:

# reshape input pattern to [samples, timesteps, features]

X
 
=
 
X
.
reshape
(
1
,
 
1
,
 
len
(
X
)
)

# make forecast

forecast
 
=
 
model
.
predict
(
X
,
 
batch_size
=
n_batch
)

# convert to array

return
 
[
x
 
for
 
x
 
in
 
forecast
[
0
,
 
:
]
]

We can call this function from the make_forecasts() function and update it to accept the model as an argument. The updated version is listed below.

我们可以从make_forecasts()中调用这个函数，并将其更新为接受模型作为参数，下面列出了更新版本

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# evaluate the persistence model

def 
make_forecasts
(
model
,
 
n_batch
,
 
train
,
 
test
,
 
n_lag
,
 
n_seq
)
:

forecasts
 
=
 
list
(
)

for
 
i
 
in
 
range
(
len
(
test
)
)
:

X
,
 
y
 
=
 
test
[
i
,
 
0
:
n_lag
]
,
 
test
[
i
,
 
n_lag
:
]

# make forecast

forecast
 
=
 
forecast_lstm
(
model
,
 
X
,
 
n_batch
)

# store the forecast

forecasts
.
append
(
forecast
)

return
 
forecasts

This updated version of the make_forecasts() function can be called as follows:

make_forecasts（）函数的更新版本可以调用如下：

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# make forecasts

forecasts
 
=
 
make_forecasts
(
model
,
 
1
,
 
train
,
 
test
,
 
1
,
 
3
)

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