NeuralForecast
class allows users to easily interact with NeuralForecast.models
PyTorch models. In this example we will forecast AirPassengers data with
a classic
LSTM
and the recent
NHITS
models. The full list of available models is available
here.
You can run these experiments using GPU with Google Colab.
1. Installing NeuralForecast
2. Loading AirPassengers Data
Thecore.NeuralForecast
class contains shared, fit
, predict
and
other methods that take as inputs pandas DataFrames with columns
['unique_id', 'ds', 'y']
, where unique_id
identifies individual time
series from the dataset, ds
is the date, and y
is the target
variable.
In this example dataset consists of a set of a single series, but you
can easily fit your model to larger datasets in long format.
unique_id | ds | y | |
---|---|---|---|
0 | 1.0 | 1949-01-31 | 112.0 |
1 | 1.0 | 1949-02-28 | 118.0 |
2 | 1.0 | 1949-03-31 | 132.0 |
3 | 1.0 | 1949-04-30 | 129.0 |
4 | 1.0 | 1949-05-31 | 121.0 |
Important DataFrames must include all['unique_id', 'ds', 'y']
columns. Make surey
column does not have missing or non-numeric values.
3. Model Training
Fit the models
Using theNeuralForecast.fit
method you can train a set of models to your dataset. You can define the
forecasting horizon
(12 in this example), and modify the
hyperparameters of the model. For example, for the
LSTM
we changed the default hidden size for both encoder and decoders.
Tip The performance of Deep Learning models can be very sensitive to the choice of hyperparameters. Tuning the correct hyperparameters is an important step to obtain the best forecasts. TheAuto
version of these models,AutoLSTM
andAutoNHITS
, already perform hyperparameter selection automatically.
Predict using the fitted models
Using theNeuralForecast.predict
method you can obtain the h
forecasts after the training data Y_df
.
NeuralForecast.predict
method returns a DataFrame with the forecasts for each unique_id
,
ds
, and model.
unique_id | ds | LSTM | NHITS | |
---|---|---|---|---|
0 | 1.0 | 1961-01-31 | 445.602112 | 447.531281 |
1 | 1.0 | 1961-02-28 | 431.253510 | 439.081024 |
2 | 1.0 | 1961-03-31 | 456.301270 | 481.924194 |
3 | 1.0 | 1961-04-30 | 508.149750 | 501.501343 |
4 | 1.0 | 1961-05-31 | 524.903870 | 514.664551 |
4. Plot Predictions
Finally, we plot the forecasts of both models againts the real values.
Tip For this guide we are using a simpleLSTM
model. More recent models, such asTSMixer
,TFT
andNHITS
achieve better accuracy thanLSTM
in most settings. The full list of available models is available here.
References
- Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio
(2020). “N-BEATS: Neural basis expansion analysis for interpretable
time series forecasting”. International Conference on Learning
Representations.
- Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski (2021). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. Accepted at AAAI 2023.