问为什么我在我的LSTM模型上得到滞后的结果？

EN

X = df[['Temperature', 'Precipitation', 'Dew', 'Wind_speed' ,'Pollution (t_1)']].values
y = df['Pollution (t)'].values
y = y.reshape(-1,1)

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(X)

#dataset has 359 samples in total
train_X, train_y = X[:278], y[:278]
test_X, test_y = X[278:], y[278:]
# 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) 


model = Sequential()
model.add(LSTM(100, input_shape=(train_X.shape[1], train_X.shape[2])))
model.add(Dropout(0.2))
#    model.add(LSTM(70))
#    model.add(Dropout(0.3))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')

history = model.fit(train_X, train_y, epochs=700, batch_size=70, validation_data=(test_X, test_y), verbose=2, shuffle=False)

# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper right')
plt.show()

from sklearn.metrics import mean_squared_error,r2_score
yhat = model.predict(test_X)
mse  = mean_squared_error(test_y, yhat)
rmse = np.sqrt(mse)
r2   = r2_score(test_y, yhat)

print("test set performance")
print("--------------------")
print("MSE:",mse)
print("RMSE:",rmse)
print("R^2: ",r2)

fig, ax = plt.subplots(figsize=(10,5))
ax.plot(range(len(test_y)), test_y, '-b',label='Actual')
ax.plot(range(len(yhat)), yhat, 'r', label='Predicted')
plt.legend()
plt.show()