> ## Documentation Index
> Fetch the complete documentation index at: https://nixtla-old-docs.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# StemGNN
The Spectral Temporal Graph Neural Network
([`StemGNN`](https://nixtlaverse.nixtla.io/neuralforecast/models.stemgnn.html#stemgnn))
is a Graph-based multivariate time-series forecasting model.
[`StemGNN`](https://nixtlaverse.nixtla.io/neuralforecast/models.stemgnn.html#stemgnn)
jointly learns temporal dependencies and inter-series correlations in
the spectral domain, by combining Graph Fourier Transform (GFT) and
Discrete Fourier Transform (DFT).
This method proved state-of-the-art performance on geo-temporal datasets
such as `Solar`, `METR-LA`, and `PEMS-BAY`, and
**References** -[Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia
Zhu, Congrui Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi
Zhang (2020). “Spectral Temporal Graph Neural Network for Multivariate
Time-series
Forecasting”.](https://proceedings.neurips.cc/paper/2020/hash/cdf6581cb7aca4b7e19ef136c6e601a5-Abstract.html)
Figure 1. StemGNN.
***
source
### GLU
> ```text theme={null}
> GLU (input_channel, output_channel)
> ```
*GLU*
***
source
### StockBlockLayer
> ```text theme={null}
> StockBlockLayer (time_step, unit, multi_layer, stack_cnt=0)
> ```
*StockBlockLayer*
***
source
### StemGNN
> ```text theme={null}
> StemGNN (h, input_size, n_series, futr_exog_list=None,
> hist_exog_list=None, stat_exog_list=None,
> exclude_insample_y=False, n_stacks=2, multi_layer:int=5,
> dropout_rate:float=0.5, leaky_rate:float=0.2, loss=MAE(),
> valid_loss=None, max_steps:int=1000, learning_rate:float=0.001,
> num_lr_decays:int=3, early_stop_patience_steps:int=-1,
> val_check_steps:int=100, batch_size:int=32,
> valid_batch_size:Optional[int]=None, windows_batch_size=32,
> inference_windows_batch_size=32, start_padding_enabled=False,
> step_size:int=1, scaler_type:str='robust', random_seed:int=1,
> drop_last_loader=False, alias:Optional[str]=None,
> optimizer=None, optimizer_kwargs=None, lr_scheduler=None,
> lr_scheduler_kwargs=None, dataloader_kwargs=None,
> **trainer_kwargs)
> ```
\*StemGNN
The Spectral Temporal Graph Neural Network
([`StemGNN`](https://nixtlaverse.nixtla.io/neuralforecast/models.stemgnn.html#stemgnn))
is a Graph-based multivariate time-series forecasting model.
[`StemGNN`](https://nixtlaverse.nixtla.io/neuralforecast/models.stemgnn.html#stemgnn)
jointly learns temporal dependencies and inter-series correlations in
the spectral domain, by combining Graph Fourier Transform (GFT) and
Discrete Fourier Transform (DFT).
**Parameters:** `h`: int, Forecast horizon. `input_size`: int,
autorregresive inputs size, y=\[1,2,3,4] input\_size=2 ->
y\_\[t-2:t]=\[1,2]. `n_series`: int, number of time-series.
`futr_exog_list`: str list, future exogenous columns.
`hist_exog_list`: str list, historic exogenous columns.
`stat_exog_list`: str list, static exogenous columns. `n_stacks`:
int=2, number of stacks in the model. `multi_layer`: int=5,
multiplier for FC hidden size on StemGNN blocks. `dropout_rate`:
float=0.5, dropout rate. `leaky_rate`: float=0.2, alpha for
LeakyReLU layer on Latent Correlation layer. `loss`: PyTorch module,
instantiated train loss class from [losses
collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).
`valid_loss`: PyTorch module=`loss`, instantiated valid loss class from
[losses
collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).
`max_steps`: int=1000, maximum number of training steps.
`learning_rate`: float=1e-3, Learning rate between (0, 1).
`num_lr_decays`: int=-1, Number of learning rate decays, evenly
distributed across max\_steps. `early_stop_patience_steps`: int=-1,
Number of validation iterations before early stopping.
`val_check_steps`: int=100, Number of training steps between every
validation loss check. `batch_size`: int, number of windows in each
batch. `valid_batch_size`: int=None, number of different series in
each validation and test batch, if None uses batch\_size.
`windows_batch_size`: int=32, number of windows to sample in each
training batch, default uses all. `inference_windows_batch_size`:
int=32, number of windows to sample in each inference batch, -1 uses
all. `start_padding_enabled`: bool=False, if True, the model will
pad the time series with zeros at the beginning, by input size.
`step_size`: int=1, step size between each window of temporal data.
`scaler_type`: str=‘robust’, type of scaler for temporal inputs
normalization see [temporal
scalers](https://nixtla.github.io/neuralforecast/common.scalers.html).
`random_seed`: int, random\_seed for pytorch initializer and numpy
generators. `drop_last_loader`: bool=False, if True
`TimeSeriesDataLoader` drops last non-full batch. `alias`: str,
optional, Custom name of the model. `optimizer`: Subclass of
‘torch.optim.Optimizer’, optional, user specified optimizer instead of
the default choice (Adam). `optimizer_kwargs`: dict, optional, list
of parameters used by the user specified `optimizer`.
`lr_scheduler`: Subclass of ‘torch.optim.lr\_scheduler.LRScheduler’,
optional, user specified lr\_scheduler instead of the default choice
(StepLR). `lr_scheduler_kwargs`: dict, optional, list of parameters
used by the user specified `lr_scheduler`. `dataloader_kwargs`:
dict, optional, list of parameters passed into the PyTorch Lightning
dataloader by the `TimeSeriesDataLoader`. `**trainer_kwargs`: int,
keyword trainer arguments inherited from [PyTorch Lighning’s
trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer). \*
***
### StemGNN.fit
> ```text theme={null}
> StemGNN.fit (dataset, val_size=0, test_size=0, random_seed=None,
> distributed_config=None)
> ```
\*Fit.
The `fit` method, optimizes the neural network’s weights using the
initialization parameters (`learning_rate`, `windows_batch_size`, …) and
the `loss` function as defined during the initialization. Within `fit`
we use a PyTorch Lightning `Trainer` that inherits the initialization’s
`self.trainer_kwargs`, to customize its inputs, see [PL’s trainer
arguments](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).
The method is designed to be compatible with SKLearn-like classes and in
particular to be compatible with the StatsForecast library.
By default the `model` is not saving training checkpoints to protect
disk memory, to get them change `enable_checkpointing=True` in
`__init__`.
**Parameters:** `dataset`: NeuralForecast’s
[`TimeSeriesDataset`](https://nixtlaverse.nixtla.io/neuralforecast/tsdataset.html#timeseriesdataset),
see
[documentation](https://nixtla.github.io/neuralforecast/tsdataset.html).
`val_size`: int, validation size for temporal cross-validation.
`random_seed`: int=None, random\_seed for pytorch initializer and numpy
generators, overwrites model.\_\_init\_\_’s. `test_size`: int, test
size for temporal cross-validation. \*
***
### StemGNN.predict
> ```text theme={null}
> StemGNN.predict (dataset, test_size=None, step_size=1, random_seed=None,
> quantiles=None, **data_module_kwargs)
> ```
\*Predict.
Neural network prediction with PL’s `Trainer` execution of
`predict_step`.
**Parameters:** `dataset`: NeuralForecast’s
[`TimeSeriesDataset`](https://nixtlaverse.nixtla.io/neuralforecast/tsdataset.html#timeseriesdataset),
see
[documentation](https://nixtla.github.io/neuralforecast/tsdataset.html).
`test_size`: int=None, test size for temporal cross-validation.
`step_size`: int=1, Step size between each window. `random_seed`:
int=None, random\_seed for pytorch initializer and numpy generators,
overwrites model.\_\_init\_\_’s. `quantiles`: list of floats,
optional (default=None), target quantiles to predict.
`**data_module_kwargs`: PL’s TimeSeriesDataModule args, see
[documentation](https://pytorch-lightning.readthedocs.io/en/1.6.1/extensions/datamodules.html#using-a-datamodule).\*
## Usage Examples
Train model and forecast future values with `predict` method.
```python theme={null}
import pandas as pd
import matplotlib.pyplot as plt
from neuralforecast import NeuralForecast
from neuralforecast.models import StemGNN
from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic
from neuralforecast.losses.pytorch import MAE
Y_train_df = AirPassengersPanel[AirPassengersPanel.ds=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test
model = StemGNN(h=12,
input_size=24,
n_series=2,
scaler_type='standard',
max_steps=500,
early_stop_patience_steps=-1,
val_check_steps=10,
learning_rate=1e-3,
loss=MAE(),
valid_loss=MAE(),
batch_size=32
)
fcst = NeuralForecast(models=[model], freq='ME')
fcst.fit(df=Y_train_df, static_df=AirPassengersStatic, val_size=12)
forecasts = fcst.predict(futr_df=Y_test_df)
# Plot predictions
fig, ax = plt.subplots(1, 1, figsize = (20, 7))
Y_hat_df = forecasts.reset_index(drop=False).drop(columns=['unique_id','ds'])
plot_df = pd.concat([Y_test_df, Y_hat_df], axis=1)
plot_df = pd.concat([Y_train_df, plot_df])
plot_df = plot_df[plot_df.unique_id=='Airline1'].drop('unique_id', axis=1)
plt.plot(plot_df['ds'], plot_df['y'], c='black', label='True')
plt.plot(plot_df['ds'], plot_df['StemGNN'], c='blue', label='Forecast')
ax.set_title('AirPassengers Forecast', fontsize=22)
ax.set_ylabel('Monthly Passengers', fontsize=20)
ax.set_xlabel('Year', fontsize=20)
ax.legend(prop={'size': 15})
ax.grid()
```
Using `cross_validation` to forecast multiple historic values.
```python theme={null}
fcst = NeuralForecast(models=[model], freq='M')
forecasts = fcst.cross_validation(df=AirPassengersPanel, static_df=AirPassengersStatic, n_windows=2, step_size=12)
# Plot predictions
fig, ax = plt.subplots(1, 1, figsize = (20, 7))
Y_hat_df = forecasts.loc['Airline1']
Y_df = AirPassengersPanel[AirPassengersPanel['unique_id']=='Airline1']
plt.plot(Y_df['ds'], Y_df['y'], c='black', label='True')
plt.plot(Y_hat_df['ds'], Y_hat_df['StemGNN'], c='blue', label='Forecast')
ax.set_title('AirPassengers Forecast', fontsize=22)
ax.set_ylabel('Monthly Passengers', fontsize=20)
ax.set_xlabel('Year', fontsize=20)
ax.legend(prop={'size': 15})
ax.grid()
```
