> ## 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. > NBEATS: Neural Basis Expansion Analysis with interpretable or generic configurations. MLP-based architecture with residual links for M3/M4 competition performance. # NBEATS The Neural Basis Expansion Analysis ([`NBEATS`](./models.nbeats.html#nbeats)) is an [`MLP`](./models.mlp.html#mlp)-based deep neural architecture with backward and forward residual links. The network has two variants: (1) in its interpretable configuration, [`NBEATS`](./models.nbeats.html#nbeats) sequentially projects the signal into polynomials and harmonic basis to learn trend and seasonality components; (2) in its generic configuration, it substitutes the polynomial and harmonic basis for identity basis and larger network’s depth. The Neural Basis Expansion Analysis with Exogenous ([`NBEATSx`](./models.nbeatsx.html#nbeatsx)), incorporates projections to exogenous temporal variables available at the time of the prediction. This method proved state-of-the-art performance on the M3, M4, and Tourism Competition datasets, improving accuracy by 3% over the `ESRNN` M4 competition winner. **References** * [Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio (2019). “N-BEATS: Neural basis expansion analysis for interpretable time series forecasting”.](https://arxiv.org/abs/1905.10437) Figure 1. Neural Basis Expansion Analysis. *Figure 1. Neural Basis Expansion Analysis.* ## NBEATS ### `NBEATS` ```python theme={null} NBEATS( h, input_size, n_harmonics=2, n_polynomials=None, n_basis=2, basis="polynomial", stack_types=["identity", "trend", "seasonality"], n_blocks=[1, 1, 1], mlp_units=3 * [[512, 512]], dropout_prob_theta=0.0, activation="ReLU", shared_weights=False, loss=MAE(), valid_loss=None, max_steps=1000, learning_rate=0.001, num_lr_decays=3, early_stop_patience_steps=-1, val_monitor="ptl/val_loss", val_check_steps=100, batch_size=32, valid_batch_size=None, windows_batch_size=1024, inference_windows_batch_size=-1, start_padding_enabled=False, training_data_availability_threshold=0.0, step_size=1, scaler_type="identity", 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) NBEATS The Neural Basis Expansion Analysis for Time Series (NBEATS), is a simple and yet effective architecture, it is built with a deep stack of MLPs with the doubly residual connections. It has a generic and interpretable architecture depending on the blocks it uses. Its interpretable architecture is recommended for scarce data settings, as it regularizes its predictions through projections unto harmonic and trend basis well-suited for most forecasting tasks. **Parameters:** `h`: int, forecast horizon. `input_size`: int, considered autorregresive inputs (lags), y=\[1,2,3,4] input\_size=2 -> lags=\[1,2]. `n_harmonics`: int, Number of harmonic terms for seasonality stack type. Note that len(n\_harmonics) = len(stack\_types). Note that it will only be used if a seasonality stack is used. `n_polynomials`: int, DEPRECATED - polynomial degree for trend stack. Note that len(n\_polynomials) = len(stack\_types). Note that it will only be used if a trend stack is used. `basis`: str, Type of basis function to use in the trend stack. Choose one from \['legendre', 'polynomial', 'changepoint', 'piecewise\_linear', 'linear\_hat', 'spline', 'chebyshev'] `n_basis`: int, the degree of the basis function for the trend stack. Note that it will only be used if a trend stack is used. `stack_types`: List\[str], List of stack types. Subset from \['seasonality', 'trend', 'identity']. `n_blocks`: List\[int], Number of blocks for each stack. Note that len(n\_blocks) = len(stack\_types). `mlp_units`: List\[List\[int]], Structure of hidden layers for each stack type. Each internal list should contain the number of units of each hidden layer. Note that len(n\_hidden) = len(stack\_types). `dropout_prob_theta`: float, Float between (0, 1). Dropout for N-BEATS basis. `activation`: str, activation from \['ReLU', 'Softplus', 'Tanh', 'SELU', 'LeakyReLU', 'PReLU', 'Sigmoid']. `shared_weights`: bool, If True, all blocks within each stack will share parameters. `loss`: PyTorch module, instantiated train loss class from [losses collection](./losses.pytorch.html). `valid_loss`: PyTorch module=`loss`, instantiated valid loss class from [losses collection](./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=3, Number of learning rate decays, evenly distributed across max\_steps. `early_stop_patience_steps`: int=-1, Number of validation iterations before early stopping. `val_monitor`: str="ptl/val\_loss", metric to monitor for early stopping. Valid options: "ptl/val\_loss", "valid\_loss", "train\_loss". `val_check_steps`: int=100, Number of training steps between every validation loss check. `batch_size`: int=32, number of different series 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=1024, number of windows to sample in each training batch, default uses all. `inference_windows_batch_size`: int=-1, 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. `training_data_availability_threshold`: Union\[float, List\[float]]=0.0, 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). `step_size`: int=1, step size between each window of temporal data. `scaler_type`: str='identity', type of scaler for temporal inputs normalization see [temporal scalers](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/common/_scalers.py). `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). **References:** -[Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio (2019). "N-BEATS: Neural basis expansion analysis for interpretable time series forecasting".](https://arxiv.org/abs/1905.10437) #### `NBEATS.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 | | #### `NBEATS.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 NBEATS 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 model = NBEATS(h=12, input_size=24, basis='changepoint', n_basis=2, loss=DistributionLoss(distribution='Poisson', level=[80, 90]), stack_types = ['identity', 'trend', 'seasonality'], max_steps=100, val_check_steps=10, early_stop_patience_steps=2) 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 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['NBEATS-median'], c='blue', label='median') plt.fill_between(x=plot_df['ds'][-12:], y1=plot_df['NBEATS-lo-90'][-12:].values, y2=plot_df['NBEATS-hi-90'][-12:].values, alpha=0.4, label='level 90') plt.grid() plt.legend() plt.plot() ```