> ## Documentation Index
> Fetch the complete documentation index at: https://nixtlaverse.nixtla.io/llms.txt
> Use this file to discover all available pages before exploring further.
> TCN: Temporal Convolutional Network with dilated causal convolutions for efficient sequential forecasting. Captures long-range dependencies with ReLU activations.
# 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.*
## TCN
### `TCN`
```python theme={null}
TCN(
h,
input_size=-1,
inference_input_size=None,
kernel_size=2,
dilations=[1, 2, 4, 8, 16],
encoder_hidden_size=128,
encoder_activation="ReLU",
context_size=10,
decoder_hidden_size=128,
decoder_layers=2,
futr_exog_list=None,
hist_exog_list=None,
stat_exog_list=None,
loss=MAE(),
valid_loss=None,
max_steps=1000,
learning_rate=0.001,
num_lr_decays=-1,
early_stop_patience_steps=-1,
val_monitor="ptl/val_loss",
val_check_steps=100,
batch_size=32,
valid_batch_size=None,
windows_batch_size=128,
inference_windows_batch_size=1024,
start_padding_enabled=False,
training_data_availability_threshold=0.0,
step_size=1,
scaler_type="robust",
random_seed=1,
drop_last_loader=False,
alias=None,
optimizer=None,
optimizer_kwargs=None,
lr_scheduler=None,
lr_scheduler_kwargs=None,
dataloader_kwargs=None,
**trainer_kwargs
)
```
Bases: [BaseModel](#neuralforecast.common._base_model.BaseModel)
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:**
| Name | Type | Description | Default |
| -------------------------------------- | -------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------- |
| `h` | [int](#int) | forecast horizon. | *required* |
| `input_size` | [int](#int) | maximum sequence length for truncated train backpropagation. Default -1 uses 3 \* horizon | -1 |
| `inference_input_size` | [int](#int) | maximum sequence length for truncated inference. Default None uses input\_size history. | None |
| `kernel_size` | [int](#int) | size of the convolving kernel. | 2 |
| `dilations` | int list | controls the temporal spacing between the kernel points; also known as the à trous algorithm. | \[1, 2, 4, 8, 16] |
| `encoder_hidden_size` | [int](#int) | units for the TCN's hidden state size. | 128 |
| `encoder_activation` | [str](#str) | type of TCN activation from `tanh` or `relu`. | 'ReLU' |
| `context_size` | [int](#int) | size of context vector for each timestamp on the forecasting window. | 10 |
| `decoder_hidden_size` | [int](#int) | size of hidden layer for the MLP decoder. | 128 |
| `decoder_layers` | [int](#int) | number of layers for the MLP decoder. | 2 |
| `futr_exog_list` | str list | future exogenous columns. | None |
| `hist_exog_list` | str list | historic exogenous columns. | None |
| `stat_exog_list` | str list | static exogenous columns. | None |
| `loss` | PyTorch module | instantiated train loss class from [losses collection](./losses.pytorch.html). | [MAE](#neuralforecast.losses.pytorch.MAE)() |
| `valid_loss` | PyTorch module | instantiated valid loss class from [losses collection](./losses.pytorch.html). | None |
| `max_steps` | [int](#int) | maximum number of training steps. | 1000 |
| `learning_rate` | [float](#float) | Learning rate between (0, 1). | 0.001 |
| `num_lr_decays` | [int](#int) | Number of learning rate decays, evenly distributed across max\_steps. | -1 |
| `early_stop_patience_steps` | [int](#int) | Number of validation iterations before early stopping. | -1 |
| `val_monitor` | [str](#str) | metric to monitor for early stopping. Valid options: "ptl/val\_loss", "valid\_loss", "train\_loss". Default: "ptl/val\_loss". | 'ptl/val\_loss' |
| `val_check_steps` | [int](#int) | Number of training steps between every validation loss check. | 100 |
| `batch_size` | [int](#int) | number of differentseries in each batch. | 32 |
| `valid_batch_size` | [int](#int) | number of different series in each validation and test batch. | None |
| `windows_batch_size` | [int](#int) | number of windows to sample in each training batch, default uses all. | 128 |
| `inference_windows_batch_size` | [int](#int) | number of windows to sample in each inference batch, -1 uses all. | 1024 |
| `start_padding_enabled` | [bool](#bool) | if True, the model will pad the time series with zeros at the beginning, by input size. | False |
| `training_data_availability_threshold` | [Union](#Union)\[[float](#float), [List](#typing.List)\[[float](#float)]] | minimum fraction of valid data points required for training windows. Single float applies to both insample and outsample; list of two floats specifies \[insample\_fraction, outsample\_fraction]. Default 0.0 allows windows with only 1 valid data point (current behavior). | 0.0 |
| `step_size` | [int](#int) | step size between each window of temporal data. | 1 |
| `scaler_type` | [str](#str) | type of scaler for temporal inputs normalization see [temporal scalers](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/common/_scalers.py). | 'robust' |
| `random_seed` | [int](#int) | random\_seed for pytorch initializer and numpy generators. | 1 |
| `drop_last_loader` | [bool](#bool) | if True `TimeSeriesDataLoader` drops last non-full batch. | False |
| `alias` | [str](#str) | optional, Custom name of the model. | None |
| `optimizer` | Subclass of 'torch.optim.Optimizer' | optional, user specified optimizer instead of the default choice (Adam). | None |
| `optimizer_kwargs` | [dict](#dict) | optional, list of parameters used by the user specified `optimizer`. | None |
| `lr_scheduler` | Subclass of 'torch.optim.lr\_scheduler.LRScheduler' | optional, user specified lr\_scheduler instead of the default choice (StepLR). | None |
| `lr_scheduler_kwargs` | [dict](#dict) | optional, list of parameters used by the user specified `lr_scheduler`. | None |
| `dataloader_kwargs` | [dict](#dict) | optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. | None |
| `**trainer_kwargs` | [int](#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`
```python theme={null}
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:**
| Name | Type | Description | Default |
| ------------- | ---------------------------------------------------- | -------------------------------------------------------------------------------------- | ----------------- |
| `dataset` | [TimeSeriesDataset](#TimeSeriesDataset) | NeuralForecast's `TimeSeriesDataset`, see [documentation](./tsdataset.html). | *required* |
| `val_size` | [int](#int) | Validation size for temporal cross-validation. | 0 |
| `random_seed` | [int](#int) | Random seed for pytorch initializer and numpy generators, overwrites model.**init**'s. | None |
| `test_size` | [int](#int) | Test size for temporal cross-validation. | 0 |
**Returns:**
| Type | Description |
| ---- | ----------- |
| None | |
#### `TCN.predict`
```python theme={null}
predict(
dataset,
test_size=None,
step_size=1,
random_seed=None,
quantiles=None,
h=None,
explainer_config=None,
**data_module_kwargs
)
```
Predict.
Neural network prediction with PL's `Trainer` execution of `predict_step`.
**Parameters:**
| Name | Type | Description | Default |
| ---------------------- | ---------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------- |
| `dataset` | [TimeSeriesDataset](#TimeSeriesDataset) | NeuralForecast's `TimeSeriesDataset`, see [documentation](./tsdataset.html). | *required* |
| `test_size` | [int](#int) | Test size for temporal cross-validation. | None |
| `step_size` | [int](#int) | Step size between each window. | 1 |
| `random_seed` | [int](#int) | Random seed for pytorch initializer and numpy generators, overwrites model.**init**'s. | None |
| `quantiles` | [list](#list) | Target quantiles to predict. | None |
| `h` | [int](#int) | Prediction horizon, if None, uses the model's fitted horizon. Defaults to None. | None |
| `explainer_config` | [dict](#dict) | configuration for explanations. | None |
| `**data_module_kwargs` | [dict](#dict) | PL's TimeSeriesDataModule args, see [documentation](https://pytorch-lightning.readthedocs.io/en/1.6.1/extensions/datamodules.html#using-a-datamodule). | {} |
**Returns:**
| Type | Description |
| ---- | ----------- |
| None | |
### 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()
```