1. LSTM Practical Exercise
R. Q. Feitosa, J. D. Bermudez, J. A. Chamorro

2. Objective Predict the value of 𝑋 𝑡+1 from past observations ( 𝑋 𝑡−2 , 𝑋 𝑡−1 , 𝑋 𝑡 )

3. Network Architecture
Architecture: Many-to-one LSTM

4. Dataset Daily USD to BRL exchange rate from April 30th 2018 to April 30th 2019.

5. Dataset Daily USD to BRL exchange rate from April 30th 2018 to April 30th 2019.
Train
Test

6. Dataset Dataset preprocessing: Sliding window
𝒙 𝟏
𝒙 𝟐
𝒙 𝟑 y
3.92
3.99
3.9
3.86
Train
Test

7. Dataset Dataset preprocessing: Sliding window
𝒙 𝟏
𝒙 𝟐
𝒙 𝟑 y
3.92
3.99
3.9
3.86
3.89
Train
Test

8. Dataset Dataset preprocessing: Sliding window
𝒙 𝟏
𝒙 𝟐
𝒙 𝟑 𝑦
3.92
3.99
3.9
3.86
3.89 …
Train
Test

9. Experimental setup Network architecture: LSTM (Many-to-one)

10. Expected Results Root Mean Squared Error Train: 0.02939 Test: 0.03424
Train prediction results
Test prediction results

11. Exercises Exercise 1: Exercise 2:
Define the LSTM network architecture according to this presentation
Exercise 2:
Evaluate the network performance in terms of RMSE and prediction graph for the next configuration modifications:
10 and 100 epochs
10 and 100 LSTM hidden units
2 LSTM layers (Deep LSTM)
Use GRU instead of LSTM