I'm playing with time series and Keras LSTM 1) bidirectional and 2) multiparallel model. I'm saving
the best model according to the "mean\_squared\_error" metrics. My dataset is normalized with MinMaxScaler (default range from 0 to 1). Mean squared error is 0.02 on the test part of the dataset. Does it mean that my model's mean error is 14% - that is 0.02^0.5. Is it a good practical interpretation of the model's precision? 

Suppose I want to predict fourth value in this sequence:

```
[10, 20, 30, 40, 50, 60, 70, 80, 90]

```

So my x\_test and y\_test looks like this:

```
[10 20 30] 40
[20 30 40] 50
[30 40 50] 60
[40 50 60] 70
[50 60 70] 80
[60 70 80] 90

```

And the code:

```
cp = [ModelCheckpoint(filepath=path+"/epochBi.h5", monitor='mean_squared_error', verbose=1, save_best_only=True)]
model.compile(optimizer='adam', loss='mse', metrics= ['mean_squared_error'])
history_callback = model.fit(X_train, y_train, epochs=200, verbose=1, callbacks=cp)
model.load_weights(path+"/epochBi.h5")
score = model.evaluate(X_test, y_test, verbose=1)

```

Suppose that I evaluate on original dataset, how would I interpret the MSE=0.02?