Hello :) Today is Day 24!
A quick summary of today:
- Using neural nets to predict ₩/$ exchange rate on Kaggle
The data I used today was also used when I made the model I made on Day 7. But then I made a regression model, and today I made a neural network model.
First of all, to predict time series, I learned that it would be good to divide the data into windows
Tuning the parameters above greatly affects the performance. It was fun to tune this parameter and model layer parameter a lot throughout the day and see how the performance was different
The final model is:
Results:
Test data results:
Below, are various models, and modifications where I wanted to see the impact of hyperparameters, so I tuned the models manually.
- model 1
MAE: 0.02755
- model 2 window_size = 183
MAE: 0.03141
- model 3 window_size = 60
MAE: 0.02798
- model 4 shuffle_buffer_size = 5000
MAE: 0.02503
- model 5 shuffle_buffer_size = 10000
MAE: 0.01939
- model 6 window_size=45
MAE: 0.03332
- model 7 window_size=60; batch_size=16
MAE: 0.02999
- model 8 batch_size=32; added another LSTM layer
MAE: 0.01627
- model 9 shuffle_buffer_size = 1000 (as it was originally)
MAE: 0.02009
- model 10 added another LSTM layer
MAE: 0.01756
- model 11 shuffle_buffer_size = 5000
MAE: 0.04496
- model 12 shuffle_buffer_size=1000 + change of model architecture
MAE: 0.02415
- model 13 changed model architecture again
MAE: 0.02625
There were other models too, but I did not take picture
That is all for today!
See you tomorrow :)