Autoencoder completed!
One of our models was trained on a compressed representation of data. The raw data corresponding to coordinates in `BGSE` format was converted into a single Real Number value using the norm of the vector.
Notes:
- BGSE:Geocentric Solar Ecliptic system. This has its X-axis pointing from the Earth toward the Sun and its Y-axis is chosen to be in the ecliptic plane pointing towards dusk (thus opposing planetary motion). Its Z-axis is parallel to the ecliptic pole. Relative to an inertial system this system has a yearly rotation.
- BGSM: Geocentric Solar Magnetospheric system. This has its X-axis from the Earth to the Sun. The Y-axis is defined to be perpendicular to the Earth's magnetic dipole so that the X-Z plane contains the dipole axis. The positive Z-axis is chosen to be in the same sense as the northern magnetic pole. The difference between the GSM and GSE systems is simply a rotation about the X-axis.
This led us to have a 2-column dataset, with the first column corresponding to the mentioned norm of the coordinates, and with the second column corresponding to BF1 values from the Wind dataset.
The autoencoder achitecture can be seen as follows:
This model was trained with ~29.7 million datapoints containing a time window of one month, with a batch size of 64 for 20 epochs, using the MSLE loss. The training history can be seen as follows:
As seen in the training history, the model was able to learn the data representation and was able to reconstruct the data with a low loss. The loss with the validation dataset after training was 5.7263e-08.
Rodolfo Ferro