> ## 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.
# TCN
For long time in deep learning, sequence modelling was synonymous with
recurrent networks, yet several papers have shown that simple
convolutional architectures can outperform canonical recurrent networks
like LSTMs by demonstrating longer effective memory. By skipping
temporal connections the causal convolution filters can be applied to
larger time spans while remaining computationally efficient.
The predictions are obtained by transforming the hidden states into
contexts $\mathbf{c}_{[t+1:t+H]}$, that are decoded and adapted into
$\mathbf{\hat{y}}_{[t+1:t+H],[q]}$ through MLPs.
where $\mathbf{h}_{t}$, is the hidden state for time $t$,
$\mathbf{y}_{t}$ is the input at time $t$ and $\mathbf{h}_{t-1}$ is the
hidden state of the previous layer at $t-1$, $\mathbf{x}^{(s)}$ are
static exogenous inputs, $\mathbf{x}^{(h)}_{t}$ historic exogenous,
$\mathbf{x}^{(f)}_{[:t+H]}$ are future exogenous available at the time
of the prediction.
**References** -[van den Oord, A., Dieleman, S., Zen, H., Simonyan,
K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A. W., &
Kavukcuoglu, K. (2016). Wavenet: A generative model for raw audio.
Computing Research Repository, abs/1609.03499. URL:
http://arxiv.org/abs/1609.03499.
arXiv:1609.03499.](https://arxiv.org/abs/1609.03499) -[Shaojie Bai,
Zico Kolter, Vladlen Koltun. (2018). An Empirical Evaluation of Generic
Convolutional and Recurrent Networks for Sequence Modeling. Computing
Research Repository, abs/1803.01271. URL:
https://arxiv.org/abs/1803.01271.](https://arxiv.org/abs/1803.01271) Figure 1. Visualization of a stack of
dilated causal convolutional layers.
***
source
### TCN
> ```text theme={null}
> TCN (h:int, input_size:int=-1, inference_input_size:Optional[int]=None,
> kernel_size:int=2, dilations:List[int]=[1, 2, 4, 8, 16],
> encoder_hidden_size:int=128, encoder_activation:str='ReLU',
> context_size:int=10, decoder_hidden_size:int=128,
> decoder_layers:int=2, futr_exog_list=None, hist_exog_list=None,
> stat_exog_list=None, loss=MAE(), valid_loss=None,
> max_steps:int=1000, learning_rate:float=0.001, num_lr_decays:int=-1,
> early_stop_patience_steps:int=-1, val_check_steps:int=100,
> batch_size:int=32, valid_batch_size:Optional[int]=None,
> windows_batch_size=128, inference_windows_batch_size=1024,
> 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)
> ```
\*TCN
Temporal Convolution Network (TCN), with MLP decoder. The historical
encoder uses dilated skip connections to obtain efficient long memory,
while the rest of the architecture allows for future exogenous
alignment.
**Parameters:** `h`: int, forecast horizon. `input_size`: int,
maximum sequence length for truncated train backpropagation. Default -1
uses 3 \* horizon `inference_input_size`: int, maximum sequence
length for truncated inference. Default None uses input\_size
history. `kernel_size`: int, size of the convolving kernel.
`dilations`: int list, ontrols the temporal spacing between the kernel
points; also known as the à trous algorithm. `encoder_hidden_size`:
int=200, units for the TCN’s hidden state size.
`encoder_activation`: str=`tanh`, type of TCN activation from `tanh` or
`relu`. `context_size`: int=10, size of context vector for each
timestamp on the forecasting window. `decoder_hidden_size`: int=200,
size of hidden layer for the MLP decoder. `decoder_layers`: int=2,
number of layers for the MLP decoder. `futr_exog_list`: str list,
future exogenous columns. `hist_exog_list`: str list, historic
exogenous columns. `stat_exog_list`: str list, static exogenous
columns. `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=32, number of
differentseries in each batch. `batch_size`: int=32, number of
differentseries in each batch. `valid_batch_size`: int=None, number
of different series in each validation and test batch.
`windows_batch_size`: int=128, number of windows to sample in each
training batch, default uses all. `inference_windows_batch_size`:
int=1024, 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=1, 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). \*
***
### TCN.fit
> ```text theme={null}
> TCN.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. \*
***
### TCN.predict
> ```text theme={null}
> TCN.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 Example
```python theme={null}
import pandas as pd
import matplotlib.pyplot as plt
from neuralforecast import NeuralForecast
from neuralforecast.models import TCN
from neuralforecast.losses.pytorch import DistributionLoss
from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic
Y_train_df = AirPassengersPanel[AirPassengersPanel.ds=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test
fcst = NeuralForecast(
models=[TCN(h=12,
input_size=-1,
loss=DistributionLoss(distribution='Normal', level=[80, 90]),
learning_rate=5e-4,
kernel_size=2,
dilations=[1,2,4,8,16],
encoder_hidden_size=128,
context_size=10,
decoder_hidden_size=128,
decoder_layers=2,
max_steps=500,
scaler_type='robust',
futr_exog_list=['y_[lag12]'],
hist_exog_list=None,
stat_exog_list=['airline1'],
)
],
freq='ME'
)
fcst.fit(df=Y_train_df, static_df=AirPassengersStatic)
forecasts = fcst.predict(futr_df=Y_test_df)
# Plot quantile predictions
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['TCN-median'], c='blue', label='median')
plt.fill_between(x=plot_df['ds'][-12:],
y1=plot_df['TCN-lo-90'][-12:].values,
y2=plot_df['TCN-hi-90'][-12:].values,
alpha=0.4, label='level 90')
plt.legend()
plt.grid()
plt.plot()
```
