更改

a = dataset[i:(i + look_back), 0]

收件人

a = dataset[i:(i + look_back), :]

如果要在训练数据中包含3个功能.

If you want the 3 features in your training data.

然后使用

model.add(LSTM(4, input_shape=(look_back,3)))

要指定您的序列中有look_back个时间步长，每个时间步长都具有3个功能.

To specify that you have look_back time steps in your sequence, each with 3 features.

它应该运行

实际上，sklearn.preprocessing.MinMaxScaler()的功能:inverse_transform()接受的输入与您安装的对象具有相同的形状.因此，您需要执行以下操作:

Indeed, sklearn.preprocessing.MinMaxScaler()'s function : inverse_transform() takes an input which has the same shape as the object you fitted. So you need to do something like this :

# Get something which has as many features as dataset
trainPredict_extended = np.zeros((len(trainPredict),3))
# Put the predictions there
trainPredict_extended[:,2] = trainPredict
# Inverse transform it and select the 3rd column.
trainPredict = scaler.inverse_transform(trainPredict_extended)[:,2]

我想您的代码中还会遇到类似以下的其他问题，但您无法解决的所有问题都没有解决:) ML部分已修复，您知道错误的出处.只需检查对象的形状，然后尝试使其匹配即可.

I guess you will have other issues like this below in your code but nothing that you can't fix :) the ML part is fixed and you know where the error comes from. Just check the shapes of your objects and try to make them match.