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Prerequisites This Guide assumes advanced familiarity with NeuralForecast. We highly recommend reading first the Getting Started and the NeuralForecast Map tutorials! Additionally, refer to the CONTRIBUTING guide for the basics how to contribute to NeuralForecast.

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Introduction

This tutorial is aimed at contributors who want to add a new model to the NeuralForecast library. The library’s existing modules handle optimization, training, selection, and evaluation of deep learning models. The core class simplifies building entire pipelines, both for industry and academia, on any dataset, with user-friendly methods such as fit and predict. Adding a new model to NeuralForecast is simpler than building a new PyTorch model from scratch. You only need to write the forward method. It has the following additional advantages:
  • Existing modules in NeuralForecast already implement the essential training and evaluating aspects for deep learning models.
  • Integrated with PyTorch-Lightning and Tune libraries for efficient optimization and distributed computation.
  • The BaseModel classes provide common optimization components, such as early stopping and learning rate schedulers.
  • Automatic performance tests are scheduled on Github to ensure quality standards.
  • Users can easily compare the performance and computation of the new model with existing models.
  • Opportunity for exposure to a large community of users and contributors.

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Example: simplified MLP model

We will present the tutorial following an example on how to add a simplified version of the current MLP model, which does not include exogenous covariates. At a given timestamp tt, the MLP model will forecast the next hh values of the univariate target time, Yt+1:t+hY_{t+1:t+h}, using as inputs the last LL historical values, given by Yt−L:tY_{t-L:t}. The following figure presents a diagram of the model.

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0. Preliminaries

Follow our tutorial on contributing here to set up your development environment. Here is a short list of the most important steps:
  1. Create a fork of the neuralforecast library.
  2. Clone the fork to your computer.
  3. Set an environment with the neuralforecast library, core dependencies, and nbdev package to code your model in an interactive notebook.

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1. Inherit the Base Class (BaseModel)

The library contains a base model class: BaseModel. Using class attributes we can make this model recurrent or not, or multivariate or univariate, or allow the use of exogenous inputs.

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a. Sampling process

During training, the base class receives a sample of time series of the dataset from the TimeSeriesLoader module. The BaseModel models will sample individual windows of size input_size+h, starting from random timestamps.

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b. BaseModel’ hyperparameters

Get familiar with the hyperparameters specified in the base class, including h (horizon), input_size, and optimization hyperparameters such as learning_rate, max_steps, among others. The following list presents the hyperparameters related to the sampling of windows:
  • h (h): number of future values to predict.
  • input_size (L): number of historic values to use as input for the model.
  • batch_size (bs): number of time series sampled by the loader during training.
  • valid_batch_size (v_bs): number of time series sampled by the loader during inference (validation and test).
  • windows_batch_size (w_bs): number of individual windows sampled during training (from the previous time series) to form the batch.
  • inference_windows_batch_size (i_bs): number of individual windows sampled during inference to form each batch. Used to control the GPU memory.

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c. Input and Output batch shapes

The forward method receives a batch of data in a dictionary with the following keys:
  • insample_y: historic values of the time series.
  • insample_mask: mask indicating the available values of the time series (1 if available, 0 if missing).
  • futr_exog: future exogenous covariates (if any).
  • hist_exog: historic exogenous covariates (if any).
  • stat_exog: static exogenous covariates (if any).
The following table presents the shape for each tensor if the attribute MULTIVARIATE = False is set:
tensorBaseModel
insample_y(w_bs, L, 1)
insample_mask(w_bs, L)
futr_exog(w_bs, L+h, n_f)
hist_exog(w_bs, L, n_h)
stat_exog(w_bs,n_s)
The forward function should return a single tensor with the forecasts of the next h timestamps for each window. Use the attributes of the loss class to automatically parse the output to the correct shape (see the example below).
Tip Since we are using nbdev, you can easily add prints to the code and see the shapes of the tensors during training.

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d. BaseModel’ methods

The BaseModel class contains several common methods for all windows-based models, simplifying the development of new models by preventing code duplication. The most important methods of the class are:
  • _create_windows: parses the time series from the TimeSeriesLoader into individual windows of size input_size+h.
  • _normalization: normalizes each window based on the scaler type.
  • _inv_normalization: inverse normalization of the forecasts.
  • training_step: training step of the model, called by PyTorch-Lightning’s Trainer class during training (fit method).
  • validation_step: validation step of the model, called by PyTorch-Lightning’s Trainer class during validation.
  • predict_step: prediction step of the model, called by PyTorch-Lightning’s Trainer class during inference (predict method).

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2. Create the model file and class

Once familiar with the basics of the BaseModel class, the next step is creating your particular model. The main steps are:
  1. Create the file in the nbs folder (https://github.com/Nixtla/neuralforecast/tree/main/nbs). It should be named models.YOUR_MODEL_NAME.ipynb.
  2. Add the header of the nbdev file.
  3. Import libraries in the file.
  4. Define the __init__ method with the model’s inherited and particular hyperparameters and instantiate the architecture.
  5. Set the following model attributes:
    • EXOGENOUS_FUTR: if the model can handle future exogenous variables (True) or not (False)
    • EXOGENOUS_HIST: if the model can handle historical exogenous variables (True) or not (False)
    • EXOGENOUS_STAT: if the model can handle static exogenous variables (True) or not (False)
    • MULTIVARIATE: If the model produces multivariate forecasts (True) or univariate (False)
    • RECURRENT: If the model produces forecasts recursively (True) or direct (False)
  6. Define the forward method, which recieves the input batch dictionary and returns the forecast.

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a. Model class

First, add the following two cells on top of the nbdev file. 
#| default_exp models.mlp
Important Change mlp to your model’s name, using lowercase and underscores. When you later run nbdev_export, it will create a YOUR_MODEL.py script in the neuralforecast/models/ directory. 
#| hide
%load_ext autoreload
%autoreload 2
Next, add the dependencies of the model. 
#| export
from typing import Optional

import torch
import torch.nn as nn

from neuralforecast.losses.pytorch import MAE
from neuralforecast.common._base_model import BaseModel
Tip Don’t forget to add the #| export tag on this cell.
Next, create the class with the init and forward methods. The following example shows the example for the simplified MLP model. We explain important details after the code. 
#| export
class MLP(BaseModel): # <<---- Inherits from BaseModel
    # Set class attributes to determine this model's characteristics
    EXOGENOUS_FUTR = False   # If the model can handle future exogenous variables
    EXOGENOUS_HIST = False   # If the model can handle historical exogenous variables
    EXOGENOUS_STAT = False   # If the model can handle static exogenous variables
    MULTIVARIATE = False    # If the model produces multivariate forecasts (True) or univariate (False)
    RECURRENT = False       # If the model produces forecasts recursively (True) or direct (False)

    def __init__(self,
                 # Inhereted hyperparameters with no defaults
                 h,
                 input_size,
                 # Model specific hyperparameters
                 num_layers = 2,
                 hidden_size = 1024,
                 # Inhereted hyperparameters with defaults
                 futr_exog_list = None,
                 hist_exog_list = None,
                 stat_exog_list = None,                 
                 exclude_insample_y = False,
                 loss = MAE(),
                 valid_loss = None,
                 max_steps: int = 1000,
                 learning_rate: float = 1e-3,
                 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 = 1024,
                 inference_windows_batch_size = -1,
                 start_padding_enabled = False,
                 step_size: int = 1,
                 scaler_type: str = 'identity',
                 random_seed: int = 1,
                 drop_last_loader: bool = False,
                 optimizer = None,
                 optimizer_kwargs = None,
                 lr_scheduler = None,
                 lr_scheduler_kwargs = None,
                 dataloader_kwargs = None,
                 **trainer_kwargs):
    # Inherit BaseWindows class
    super(MLP, self).__init__(h=h,
                              input_size=input_size,
                              ..., # <<--- Add all inhereted hyperparameters
                              random_seed=random_seed,
                              **trainer_kwargs)

    # Architecture
    self.num_layers = num_layers
    self.hidden_size = hidden_size

    # MultiLayer Perceptron
    layers = [nn.Linear(in_features=input_size, out_features=hidden_size)]
    layers += [nn.ReLU()]
    for i in range(num_layers - 1):
        layers += [nn.Linear(in_features=hidden_size, out_features=hidden_size)]
        layers += [nn.ReLU()]
    self.mlp = nn.ModuleList(layers)

    # Adapter with Loss dependent dimensions
    self.out = nn.Linear(in_features=hidden_size, 
                         out_features=h * self.loss.outputsize_multiplier) ## <<--- Use outputsize_multiplier to adjust output size

    def forward(self, windows_batch): # <<--- Receives windows_batch dictionary
        # Parse windows_batch
        insample_y = windows_batch['insample_y'].squeeze(-1)                            # [batch_size, input_size]
        # MLP
        hidden = self.mlp(insample_y)                                                   # [batch_size, hidden_size]
        y_pred = self.out(hidden)                                                       # [batch_size, h * n_outputs]
        
        # Reshape
        y_pred = y_pred.reshape(batch_size, self.h, self.loss.outputsize_multiplier)    # [batch_size, h, n_outputs]

        return y_pred
Tip
  • Don’t forget to add the #| export tag on each cell.
  • Larger architectures, such as Transformers, might require splitting the forward by using intermediate functions.

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Important notes

The base class has many hyperparameters, and models must have default values for all of them (except h and input_size). If you are unsure of what default value to use, we recommend copying the default values from existing models for most optimization and sampling hyperparameters. You can change the default values later at any time. The reshape method at the end of the forward step is used to adjust the output shape. The loss class contains an outputsize_multiplier attribute to automatically adjust the output size of the forecast depending on the loss. For example, for the Multi-quantile loss (MQLoss), the model needs to output each quantile for each horizon.

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b. Tests and documentation

nbdev allows for testing and documenting the model during the development process. It allows users to iterate the development within the notebook, testing the code in the same environment. Refer to existing models, such as the complete MLP model here. These files already contain the tests, documentation, and usage examples that were used during the development process.

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c. Export the new model to the library with nbdev

Following the CONTRIBUTING guide, the next step is to export the new model from the development notebook to the neuralforecast folder with the actual scripts. To export the model, run nbdev_export in your terminal. You should see a new file with your model in the neuralforecast/models/ folder.

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3. Core class and additional files

Finally, add the model to the core class and additional files:
  1. Manually add the model in the following init file.
  2. Add the model to the core class, using the nbdev file here:
    1. Add the model to the initial model list:
    from neuralforecast.models import (
GRU, LSTM, RNN, TCN, DilatedRNN,
MLP, NHITS, NBEATS, NBEATSx,
TFT, VanillaTransformer,
Informer, Autoformer, FEDformer,
StemGNN, PatchTST
)
    1. Add the model to the MODEL_FILENAME_DICT dictionary (used for the save and load functions).

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4. Add the model to the documentation

It’s important to add the model to the necessary documentation pages so that everyone can find the documentation:
  1. Add the model to the model overview table.
  2. Add the model to mint.json.

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5. Upload to GitHub

Congratulations! The model is ready to be used in the library following the steps above. Follow our contributing guide’s final steps to upload the model to GitHub: here. One of the maintainers will review the PR, request changes if necessary, and merge it into the library.

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Quick Checklist

  • Get familiar with the BaseModel class hyperparameters and input/output shapes of the forward method.
  • Create the notebook with your model class in the nbs folder: models.YOUR_MODEL_NAME.ipynb
  • Add the header and import libraries.
  • Implement init and forward methods and set the class attributes.
  • Export model with nbdev_export.
  • Add model to this init file.
  • Add the model to the core class here.
  • Follow the CONTRIBUTING guide to create the PR to upload the model.