Python 使用双向LSTM进行长期预测，如何进一步预测2年？

我已经训练了一个LSTM模型，并且想预测未来2年？列车数据是一组5年的每周销售数据

df=pd.read_csv('data.csv')

subplotindex=0
numrows=4
numcols=2
fig, ax = plt.subplots(numrows, numcols, figsize=(18,15))
plt.subplots_adjust(wspace=0.1, hspace=0.3)

warnings.filterwarnings("ignore")

r=['product1','product2']

for x in r:
    rowindex=math.floor(subplotindex/numcols)
    colindex=subplotindex-(rowindex*numcols)
    X=df[x].values
    scaler = MinMaxScaler(feature_range = (0, 1))
    X=scaler.fit_transform(X.reshape(-1, 1))
    X_train,y_train=split_sequence(X[0:size], n_steps)
    X_test,y_test=split_sequence(X[size:len(df)], n_steps)
    X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], n_features))

    model = Sequential()
    model.add(Bidirectional(LSTM(52, activation='relu'), input_shape=(n_steps, n_features)))
    model.add(Dense(1))
    model.compile(optimizer='adam', loss='mse')
    model.fit(X_train, y_train, epochs=400, verbose=0)

    X_test = X_test.reshape((len(X_test), n_steps, n_features))
    predictions = model.predict(X_test, verbose=0)

    y_test=scaler.inverse_transform(y_test)
    predictions = scaler.inverse_transform(predictions)

    error = mean_squared_error(y_test, predictions)
    perror = mean_absolute_percentage_error(y_test, predictions)
    resultsLongtermdf.loc['Bidirectional LSTM MSE',x]=error
    resultsLongtermdf.loc['Bidirectional LSTM MAPE',x]=perror
    ax[rowindex,colindex].set_title(x+' (MSE=' + str(round(error,2))+', MAPE='+ str(round(perror,2)) +'%)')
    ax[rowindex,colindex].legend(['Real', 'Predicted'], loc='upper left')
    ax[rowindex,colindex].plot(y_test)
    ax[rowindex,colindex].plot(predictions, color='red')
    subplotindex=subplotindex+1
plt.show()

那么，我如何将预测设置为未来部分呢

请你也给我解释一下，为什么是逆变换部分

y_test=scaler.inverse_transform(y_test)