LSTM Timeseries recursive prediction converge to same value I'm working on Timeseries sequence prediction using LSTM. 
My goal is to use window of 25 past values in order to generate a prediction for the next 25 values. I'm doing that recursively:
I use 25 known values to predict the next value. Append that value as know value then shift the 25 values and predict the next one again until i have 25 new generated values (or more) 

I'm using "Keras" to implement the RNN
Architecture:

regressor = Sequential()
regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))
regressor.add(Dropout(0.1))

regressor.add(LSTM(units = 50, return_sequences = True))
regressor.add(Dropout(0.1))

regressor.add(LSTM(units = 50))
regressor.add(Dropout(0.1))

regressor.add(Dense(units = 1))

regressor.compile(optimizer = 'rmsprop', loss = 'mean_squared_error')

regressor.fit(X_train, y_train, epochs = 10, batch_size = 32)


Problem: 
Recursive prediction always converge to the some value no matter what sequence comes before.





For sure this is not what I want, I was expecting that the generated sequence will be different depending on what I have before and I'm wondering if someone have an idea about this behavior and how to avoid it. Maybe I'm doing something wrong ...

I tried different epochs number and didn't help much, actually more epochs made it worse. Changing Batch Size, Number of Units , Number of Layers , and window size didn't help too in avoiding this issue. 

I'm using MinMaxScaler for the data.
If you want to use an LSTM for time series prediction, you have to try every possible parameter to find the best possible result. In order to check your model performance, you should also check your train/validation loss (this will probably show that 10 epochs is too few). Check your network architecture. Why do you use three layers? More layers won't mean better results. Try different optimizers with different learning rates. Finally, ask yourself why you are even using an LSTM for prediction? Have you tried other methods? LSTMs aren't magic, they won't have perfect prediction without any pattern to draw from, and you have pretty small samples for training.