我已按照this example的说明进行操作，一切似乎都正常，但我遇到的一个问题涉及最终的inverse_transform（）函数 .
具体来说，我希望一旦我反映了预测值和实际值的缩放比例，在我标准化和转换我的数据集之前，输出将与原始数据的单位相同 . 我试图预测的变量的平均值约为29.186，但是当我运行inverse_transform（）并根据实际值绘制预测时，我的结果输出范围为3.30 - 3.55 .
可能是我做了一个简单的错误切片数组，或者它可能是完全不同的东西，但我似乎无法确定为什么我的预测值和实际值不在同一单位的根本原因在改变一切之前的数据 .
对于其他上下文，这是train_X.shape，train_y.shape，test_X.shape，test_y.shape的打印输出：
(1804950, 1, 22) (1804950,) (849389, 1, 22) (849389,)
这是我的代码的相关部分：
# ensure all data is float
values = ips_data.values.astype('float32')
# normalize features
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(values)
# frame as supervised learning
ips_reframed = series_to_supervised(scaled, 1, 1)
# drop a bunch of columns I don't want to predict
ips_reframed.drop(ips_reframed.columns[[22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,42,43]], axis=1, inplace=True)
# print(ips_reframed.head())
# split into train and test sets
values = ips_reframed.values
# n_train_hours = (365 * 24 * 60) * 3
n_train_hours = int(len(values) * 0.68)
train = values[:n_train_hours, :]
test = values[n_train_hours:, :]
# split into input and outputs
train_X, train_y = train[:, :-1], train[:, -1]
test_X, test_y = test[:, :-1], test[:, -1]
# reshape input to be 3D [samples, timesteps, features]
train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))
test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))
# print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)
# design network
model = Sequential()
model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))
model.add(Dropout(0.5))
model.add(LSTM(50))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation("linear"))
model.compile(loss='mae', optimizer='adam')
# fit network
history = model.fit(train_X, train_y, epochs=100, batch_size=1000,
validation_data=(test_X, test_y), verbose=2, shuffle=False)
# plot history
plt.plot(history.history['loss'], label='train')
plt.plot(history.history['val_loss'], label='test')
plt.legend()
plt.show()
# make a prediction
yhat = model.predict(test_X)
# invert scaling for forecast
test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))
inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1)
inv_yhat = scaler.inverse_transform(inv_yhat)
inv_yhat = inv_yhat[:,0]
# invert scaling for actual
test_y = test_y.reshape((len(test_y), 1))
inv_y = concatenate((test_y, test_X[:, 1:]), axis=1)
inv_y = scaler.inverse_transform(inv_y)
inv_y = inv_y[:,0]
# calculate RMSE
rmse = sqrt(mean_squared_error(inv_y, inv_yhat))
# print('Test RMSE: %.3f' % rmse)
# plotting the predictions
plt.plot(inv_yhat[-100:], label='predictions')
plt.plot(inv_y[-100:], label='actual')
plt.title("Prediction vs. Actual")
plt.legend()
plt.show()
如果您需要任何其他信息或背景，请告诉我，非常感谢您的帮助！