This repository is for comparing a Mamba model to a LSTM model for weather prediction timeseries data.
The model is trained on 8 varaibles from the GFS 0.25 degree dataset. The training dataset has a sample size of 36,160 and the test dataset has a sample size of 9,040.
Variables
- Temperature
- Surface pressure
- V component of wind
- U component of wind
- Specific humidity
- Convective precipitation
- Total precipitation
- Water equivalent of accumulated snow depth
At each time step, data is taken from 200 coordinated. The data is normalized to fit within the range of -1 to 1.
I trained two models, a Mamba and a LSTM. Both models have the save parameters:
- Hidden dimensions: 512
- Number of layers: 3
| Variable | Mamba (MSE) | LSTM (MSE) |
|---|---|---|
| Temperature | 1.6630e-05 | 1.6136e-05 |
| Surface presure | 5.1565e-05 | 7.3468e-05 |
| V component of wind | 0.0008 | 0.0023 |
| U component of wind | 0.0003 | 0.0020 |
| Specific humidity | 0.0002 | 0.0009 |
| Convective precipitation | 5.1313e-05 | 6.3685e-05 |
| Total precipitation | 3.4177e-05 | 4.7444e-05 |
| Water equivalent of accumulated snow depth | 1.4074e-06 | 1.1085e-12 |
| Average | 0.00018 | 0.00068 |
Lower MSE is better and shown in bold