# LSTM model for multi-step forecasting with multivariate time series

Im am trying to do a multi-step forecasting with multivariate time series, I have 9 variables (Y,X1,..X8) with 2270 samples for each variable, and I am trying to predict the future values of Y (70 future values).

I am wondering how far can i get a good accuracy? I used an lstm model but a get a very low accuracy: 15%!

I normalized my data and I am using a window size of 120.

I used the code provided by tensorflow documentation : https://www.tensorflow.org/tutorials/structured_data/time_series

my data:

my predictions:

LSTM :

    multi_step_model = tf.keras.models.Sequential()