N-BEATS¶
- class darts.models.forecasting.nbeats.NBEATSModel(input_chunk_length, output_chunk_length, generic_architecture=True, num_stacks=30, num_blocks=1, num_layers=4, layer_widths=256, expansion_coefficient_dim=5, trend_polynomial_degree=2, dropout=0.0, activation='ReLU', **kwargs)[source]¶
Bases:
darts.models.forecasting.torch_forecasting_model.PastCovariatesTorchModel
Neural Basis Expansion Analysis Time Series Forecasting (N-BEATS).
This is an implementation of the N-BEATS architecture, as outlined in [1].
In addition to the univariate version presented in the paper, our implementation also supports multivariate series (and covariates) by flattening the model inputs to a 1-D series and reshaping the outputs to a tensor of appropriate dimensions. Furthermore, it also supports producing probabilistic forecasts (by specifying a likelihood parameter).
This model supports past covariates (known for input_chunk_length points before prediction time).
- Parameters
input_chunk_length (
int
) – The length of the input sequence fed to the model.output_chunk_length (
int
) – The length of the forecast of the model.generic_architecture (
bool
) – Boolean value indicating whether the generic architecture of N-BEATS is used. If not, the interpretable architecture outlined in the paper (consisting of one trend and one seasonality stack with appropriate waveform generator functions).num_stacks (
int
) – The number of stacks that make up the whole model. Only used if generic_architecture is set to True. The interpretable architecture always uses two stacks - one for trend and one for seasonality.num_blocks (
int
) – The number of blocks making up every stack.num_layers (
int
) – The number of fully connected layers preceding the final forking layers in each block of every stack.layer_widths (
Union
[int
,List
[int
]]) – Determines the number of neurons that make up each fully connected layer in each block of every stack. If a list is passed, it must have a length equal to num_stacks and every entry in that list corresponds to the layer width of the corresponding stack. If an integer is passed, every stack will have blocks with FC layers of the same width.expansion_coefficient_dim (
int
) – The dimensionality of the waveform generator parameters, also known as expansion coefficients. Only used if generic_architecture is set to True.trend_polynomial_degree (
int
) – The degree of the polynomial used as waveform generator in trend stacks. Only used if generic_architecture is set to False.dropout (
float
) – The dropout probability to be used in fully connected layers. This is compatible with Monte Carlo dropout at inference time for model uncertainty estimation (enabled withmc_dropout=True
at prediction time).activation (
str
) – The activation function of encoder/decoder intermediate layer (default=’ReLU’). Supported activations: [‘ReLU’,’RReLU’, ‘PReLU’, ‘Softplus’, ‘Tanh’, ‘SELU’, ‘LeakyReLU’, ‘Sigmoid’]**kwargs – Optional arguments to initialize the pytorch_lightning.Module, pytorch_lightning.Trainer, and Darts’
TorchForecastingModel
.loss_fn – PyTorch loss function used for training. This parameter will be ignored for probabilistic models if the
likelihood
parameter is specified. Default:torch.nn.MSELoss()
.likelihood – One of Darts’
Likelihood
models to be used for probabilistic forecasts. Default:None
.torch_metrics – A torch metric or a
MetricCollection
used for evaluation. A full list of available metrics can be found at https://torchmetrics.readthedocs.io/en/latest/. Default:None
.optimizer_cls – The PyTorch optimizer class to be used. Default:
torch.optim.Adam
.optimizer_kwargs – Optionally, some keyword arguments for the PyTorch optimizer (e.g.,
{'lr': 1e-3}
for specifying a learning rate). Otherwise the default values of the selectedoptimizer_cls
will be used. Default:None
.lr_scheduler_cls – Optionally, the PyTorch learning rate scheduler class to be used. Specifying
None
corresponds to using a constant learning rate. Default:None
.lr_scheduler_kwargs – Optionally, some keyword arguments for the PyTorch learning rate scheduler. Default:
None
.use_reversible_instance_norm – Whether to use reversible instance normalization RINorm against distribution shift as shown in [2]. It is only applied to the features of the target series and not the covariates.
batch_size – Number of time series (input and output sequences) used in each training pass. Default:
32
.n_epochs – Number of epochs over which to train the model. Default:
100
.model_name – Name of the model. Used for creating checkpoints and saving tensorboard data. If not specified, defaults to the following string
"YYYY-mm-dd_HH_MM_SS_torch_model_run_PID"
, where the initial part of the name is formatted with the local date and time, while PID is the processed ID (preventing models spawned at the same time by different processes to share the same model_name). E.g.,"2021-06-14_09_53_32_torch_model_run_44607"
.work_dir – Path of the working directory, where to save checkpoints and Tensorboard summaries. Default: current working directory.
log_tensorboard – If set, use Tensorboard to log the different parameters. The logs will be located in:
"{work_dir}/darts_logs/{model_name}/logs/"
. Default:False
.nr_epochs_val_period – Number of epochs to wait before evaluating the validation loss (if a validation
TimeSeries
is passed to thefit()
method). Default:1
.force_reset – If set to
True
, any previously-existing model with the same name will be reset (all checkpoints will be discarded). Default:False
.save_checkpoints – Whether or not to automatically save the untrained model and checkpoints from training. To load the model from checkpoint, call
MyModelClass.load_from_checkpoint()
, whereMyModelClass
is theTorchForecastingModel
class that was used (such asTFTModel
,NBEATSModel
, etc.). If set toFalse
, the model can still be manually saved usingsave()
and loaded usingload()
. Default:False
.add_encoders –
A large number of past and future covariates can be automatically generated with add_encoders. This can be done by adding multiple pre-defined index encoders and/or custom user-made functions that will be used as index encoders. Additionally, a transformer such as Darts’
Scaler
can be added to transform the generated covariates. This happens all under one hood and only needs to be specified at model creation. ReadSequentialEncoder
to find out more aboutadd_encoders
. Default:None
. An example showing some ofadd_encoders
features:def encode_year(idx): return (idx.year - 1950) / 50 add_encoders={ 'cyclic': {'future': ['month']}, 'datetime_attribute': {'future': ['hour', 'dayofweek']}, 'position': {'past': ['relative'], 'future': ['relative']}, 'custom': {'past': [encode_year]}, 'transformer': Scaler() }
random_state – Control the randomness of the weights initialization. Check this link for more details. Default:
None
.pl_trainer_kwargs –
- By default
TorchForecastingModel
creates a PyTorch Lightning Trainer with several useful presets that performs the training, validation and prediction processes. These presets include automatic checkpointing, tensorboard logging, setting the torch device and more. With
pl_trainer_kwargs
you can add additional kwargs to instantiate the PyTorch Lightning trainer object. Check the PL Trainer documentation for more information about the supported kwargs. Default:None
. Running on GPU(s) is also possible usingpl_trainer_kwargs
by specifying keys"accelerator", "devices", and "auto_select_gpus"
. Some examples for setting the devices inside thepl_trainer_kwargs
dict:
{"accelerator": "cpu"}
for CPU,{"accelerator": "gpu", "devices": [i]}
to use only GPUi
(i
must be an integer),{"accelerator": "gpu", "devices": -1, "auto_select_gpus": True}
to use all available GPUS.
For more info, see here: https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html#trainer-flags , and https://pytorch-lightning.readthedocs.io/en/stable/accelerators/gpu_basic.html#train-on-multiple-gpus
With parameter
"callbacks"
you can add custom or PyTorch-Lightning built-in callbacks to Darts’TorchForecastingModel
. Below is an example for adding EarlyStopping to the training process. The model will stop training early if the validation loss val_loss does not improve beyond specifications. For more information on callbacks, visit: PyTorch Lightning Callbacksfrom pytorch_lightning.callbacks.early_stopping import EarlyStopping # stop training when validation loss does not decrease more than 0.05 (`min_delta`) over # a period of 5 epochs (`patience`) my_stopper = EarlyStopping( monitor="val_loss", patience=5, min_delta=0.05, mode='min', ) pl_trainer_kwargs={"callbacks": [my_stopper]}
Note that you can also use a custom PyTorch Lightning Trainer for training and prediction with optional parameter
trainer
infit()
andpredict()
.- By default
- show_warnings
whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of your forecasting use case. Default:
False
.
References
- 1
- 2
T. Kim et al. “Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift”, https://openreview.net/forum?id=cGDAkQo1C0p
Examples
>>> from darts.datasets import WeatherDataset >>> from darts.models import NBEATSModel >>> series = WeatherDataset().load() >>> # predicting atmospheric pressure >>> target = series['p (mbar)'][:100] >>> # optionally, use past observed rainfall (pretending to be unknown beyond index 100) >>> past_cov = series['rain (mm)'][:100] >>> # changing the activation function of the encoder/decoder to LeakyReLU >>> model = NBEATSModel( >>> input_chunk_length=6, >>> output_chunk_length=6, >>> n_epochs=5, >>> activation='LeakyReLU' >>> ) >>> model.fit(target, past_covariates=past_cov) >>> pred = model.predict(6) >>> pred.values() array([[ 929.78509085], [1013.66339481], [ 999.8843893 ], [ 892.66032082], [ 921.09781534], [ 950.37965429]])
Note
NBEATS example notebook presents techniques that can be used to improve the forecasts quality compared to this simple usage example.
Attributes
Whether the model considers static covariates, if there are any.
A 6-tuple containing in order: (min target lag, max target lag, min past covariate lag, max past covariate lag, min future covariate lag, max future covariate lag).
The minimum number of samples for training the model.
Number of time steps predicted at once by the model, not defined for statistical models.
Whether model instance supports direct prediction of likelihood parameters
Whether the model considers more than one variate in the time series.
Whether the model supports optimized historical forecasts
Whether model supports past covariates
Whether model supports static covariates
Whether the model uses future covariates, once fitted.
Whether the model uses past covariates, once fitted.
Whether the model uses static covariates, once fitted.
epochs_trained
input_chunk_length
likelihood
model_created
model_params
Methods
backtest
(series[, past_covariates, ...])Compute error values that the model would have produced when used on (potentially multiple) series.
fit
(series[, past_covariates, ...])Fit/train the model on one or multiple series.
fit_from_dataset
(train_dataset[, ...])Train the model with a specific
darts.utils.data.TrainingDataset
instance.generate_fit_encodings
(series[, ...])Generates the covariate encodings that were used/generated for fitting the model and returns a tuple of past, and future covariates series with the original and encoded covariates stacked together.
generate_fit_predict_encodings
(n, series[, ...])Generates covariate encodings for training and inference/prediction and returns a tuple of past, and future covariates series with the original and encoded covariates stacked together.
generate_predict_encodings
(n, series[, ...])Generates covariate encodings for the inference/prediction set and returns a tuple of past, and future covariates series with the original and encoded covariates stacked together.
gridsearch
(parameters, series[, ...])Find the best hyper-parameters among a given set using a grid search.
historical_forecasts
(series[, ...])Compute the historical forecasts that would have been obtained by this model on (potentially multiple) series.
load
(path, **kwargs)Loads a model from a given file path.
load_from_checkpoint
(model_name[, work_dir, ...])Load the model from automatically saved checkpoints under '{work_dir}/darts_logs/{model_name}/checkpoints/'.
load_weights
(path[, load_encoders, skip_checks])Loads the weights from a manually saved model (saved with
save()
).load_weights_from_checkpoint
([model_name, ...])Load only the weights from automatically saved checkpoints under '{work_dir}/darts_logs/{model_name}/ checkpoints/'.
lr_find
(series[, past_covariates, ...])A wrapper around PyTorch Lightning's Tuner.lr_find().
predict
(n[, series, past_covariates, ...])Predict the
n
time step following the end of the training series, or of the specifiedseries
.predict_from_dataset
(n, input_series_dataset)This method allows for predicting with a specific
darts.utils.data.InferenceDataset
instance.Resets the model object and removes all stored data - model, checkpoints, loggers and training history.
residuals
(series[, past_covariates, ...])Compute the residuals produced by this model on a (or sequence of) univariate time series.
save
([path])Saves the model under a given path.
to_cpu
()Updates the PyTorch Lightning Trainer parameters to move the model to CPU the next time :fun:`fit()` or
predict()
is called.- backtest(series, past_covariates=None, future_covariates=None, historical_forecasts=None, num_samples=1, train_length=None, start=None, start_format='value', forecast_horizon=1, stride=1, retrain=True, overlap_end=False, last_points_only=False, metric=<function mape>, reduction=<function mean>, verbose=False, show_warnings=True)¶
Compute error values that the model would have produced when used on (potentially multiple) series.
If historical_forecasts are provided, the metric (given by the metric function) is evaluated directly on the forecast and the actual values. Otherwise, it repeatedly builds a training set: either expanding from the beginning of series or moving with a fixed length train_length. It trains the current model on the training set, emits a forecast of length equal to forecast_horizon, and then moves the end of the training set forward by stride time steps. The metric is then evaluated on the forecast and the actual values. Finally, the method returns a reduction (the mean by default) of all these metric scores.
By default, this method uses each historical forecast (whole) to compute error scores. If last_points_only is set to True, it will use only the last point of each historical forecast. In this case, no reduction is used.
By default, this method always re-trains the models on the entire available history, corresponding to an expanding window strategy. If retrain is set to False (useful for models for which training might be time-consuming, such as deep learning models), the trained model will be used directly to emit the forecasts.
- Parameters
series (
Union
[TimeSeries
,Sequence
[TimeSeries
]]) – The (or a sequence of) target time series used to successively train and evaluate the historical forecasts.past_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, one (or a sequence of) past-observed covariate series. This applies only if the model supports past covariates.future_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, one (or a sequence of) future-known covariate series. This applies only if the model supports future covariates.historical_forecasts (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, the (or a sequence of) historical forecasts time series to be evaluated. Corresponds to the output ofhistorical_forecasts()
. If provided, will skip historical forecasting and ignore all parameters except series, metric, and reduction.num_samples (
int
) – Number of times a prediction is sampled from a probabilistic model. Use values >1 only for probabilistic models.train_length (
Optional
[int
]) – Number of time steps in our training set (size of backtesting window to train on). Only effective when retrain is notFalse
. Default is set to train_length=None where it takes all available time steps up until prediction time, otherwise the moving window strategy is used. If larger than the number of time steps available, all steps up until prediction time are used, as in default case. Needs to be at least min_train_series_length.start (
Union
[Timestamp
,float
,int
,None
]) –Optionally, the first point in time at which a prediction is computed. This parameter supports:
float
,int
,pandas.Timestamp
, andNone
. If afloat
, it is the proportion of the time series that should lie before the first prediction point. If anint
, it is either the index position of the first prediction point for series with a pd.DatetimeIndex, or the index value for series with a pd.RangeIndex. The latter can be changed to the index position with start_format=”position”. If apandas.Timestamp
, it is the time stamp of the first prediction point. IfNone
, the first prediction point will automatically be set to:the first predictable point if retrain is
False
, or retrain is a Callable and the first predictable point is earlier than the first trainable point.the first trainable point if retrain is
True
orint
(given train_length), or retrain is a Callable and the first trainable point is earlier than the first predictable point.the first trainable point (given train_length) otherwise
Note: Raises a ValueError if start yields a time outside the time index of series. Note: If start is outside the possible historical forecasting times, will ignore the parameter (default behavior with
None
) and start at the first trainable/predictable point.start_format (
Literal
[‘position’, ‘value’]) – Defines the start format. Only effective when start is an integer and series is indexed with a pd.RangeIndex. If set to ‘position’, start corresponds to the index position of the first predicted point and can range from (-len(series), len(series) - 1). If set to ‘value’, start corresponds to the index value/label of the first predicted point. Will raise an error if the value is not in series’ index. Default:'value'
forecast_horizon (
int
) – The forecast horizon for the point predictions.stride (
int
) – The number of time steps between two consecutive predictions.retrain (
Union
[bool
,int
,Callable
[…,bool
]]) –Whether and/or on which condition to retrain the model before predicting. This parameter supports 3 different datatypes:
bool
, (positive)int
, andCallable
(returning abool
). In the case ofbool
: retrain the model at each step (True), or never retrains the model (False). In the case ofint
: the model is retrained every retrain iterations. In the case ofCallable
: the model is retrained whenever callable returns True. The callable must have the following positional arguments:counter (int): current retrain iteration
pred_time (pd.Timestamp or int): timestamp of forecast time (end of the training series)
train_series (TimeSeries): train series up to pred_time
past_covariates (TimeSeries): past_covariates series up to pred_time
future_covariates (TimeSeries): future_covariates series up to min(pred_time + series.freq * forecast_horizon, series.end_time())
Note: if any optional *_covariates are not passed to historical_forecast,
None
will be passed to the corresponding retrain function argument. Note: some models do require being retrained every time and do not support anything other than retrain=True.overlap_end (
bool
) – Whether the returned forecasts can go beyond the series’ end or not.last_points_only (
bool
) – Whether to use the whole historical forecasts or only the last point of each forecast to compute the error.metric (
Union
[Callable
[[TimeSeries
,TimeSeries
],float
],List
[Callable
[[TimeSeries
,TimeSeries
],float
]]]) – A function or a list of function that takes twoTimeSeries
instances as inputs and returns an error value.reduction (
Optional
[Callable
[[ndarray
],float
]]) – A function used to combine the individual error scores obtained when last_points_only is set to False. When providing several metric functions, the function will receive the argument axis = 0 to obtain single value for each metric function. If explicitly set to None, the method will return a list of the individual error scores instead. Set tonp.mean
by default.verbose (
bool
) – Whether to print progress.show_warnings (
bool
) – Whether to show warnings related to parameters start, and train_length.
- Returns
The (sequence of) error score on a series, or list of list containing error scores for each provided series and each sample.
- Return type
float or List[float] or List[List[float]]
- property considers_static_covariates: bool¶
Whether the model considers static covariates, if there are any.
- Return type
bool
- property epochs_trained: int¶
- Return type
int
- property extreme_lags: Tuple[Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], Optional[int]]¶
A 6-tuple containing in order: (min target lag, max target lag, min past covariate lag, max past covariate lag, min future covariate lag, max future covariate lag). If 0 is the index of the first prediction, then all lags are relative to this index.
See examples below.
- If the model wasn’t fitted with:
target (concerning RegressionModels only): then the first element should be None.
past covariates: then the third and fourth elements should be None.
future covariates: then the fifth and sixth elements should be None.
Should be overridden by models that use past or future covariates, and/or for model that have minimum target lag and maximum target lags potentially different from -1 and 0.
Notes
maximum target lag (second value) cannot be None and is always larger than or equal to 0.
Examples
>>> model = LinearRegressionModel(lags=3, output_chunk_length=2) >>> model.fit(train_series) >>> model.extreme_lags (-3, 1, None, None, None, None) >>> model = LinearRegressionModel(lags=[-3, -5], lags_past_covariates = 4, output_chunk_length=7) >>> model.fit(train_series, past_covariates=past_covariates) >>> model.extreme_lags (-5, 6, -4, -1, None, None) >>> model = LinearRegressionModel(lags=[3, 5], lags_future_covariates = [4, 6], output_chunk_length=7) >>> model.fit(train_series, future_covariates=future_covariates) >>> model.extreme_lags (-5, 6, None, None, 4, 6) >>> model = NBEATSModel(input_chunk_length=10, output_chunk_length=7) >>> model.fit(train_series) >>> model.extreme_lags (-10, 6, None, None, None, None) >>> model = NBEATSModel(input_chunk_length=10, output_chunk_length=7, lags_future_covariates=[4, 6]) >>> model.fit(train_series, future_covariates) >>> model.extreme_lags (-10, 6, None, None, 4, 6)
- Return type
Tuple
[Optional
[int
],Optional
[int
],Optional
[int
],Optional
[int
],Optional
[int
],Optional
[int
]]
- fit(series, past_covariates=None, future_covariates=None, val_series=None, val_past_covariates=None, val_future_covariates=None, trainer=None, verbose=None, epochs=0, max_samples_per_ts=None, num_loader_workers=0)¶
Fit/train the model on one or multiple series.
This method wraps around
fit_from_dataset()
, constructing a default training dataset for this model. If you need more control on how the series are sliced for training, consider callingfit_from_dataset()
with a customdarts.utils.data.TrainingDataset
.Training is performed with a PyTorch Lightning Trainer. It uses a default Trainer object from presets and
pl_trainer_kwargs
used at model creation. You can also use a custom Trainer with optional parametertrainer
. For more information on PyTorch Lightning Trainers check out this link .This function can be called several times to do some extra training. If
epochs
is specified, the model will be trained for some (extra)epochs
epochs.Below, all possible parameters are documented, but not all models support all parameters. For instance, all the
PastCovariatesTorchModel
support onlypast_covariates
and notfuture_covariates
. Darts will complain if you try fitting a model with the wrong covariates argument.When handling covariates, Darts will try to use the time axes of the target and the covariates to come up with the right time slices. So the covariates can be longer than needed; as long as the time axes are correct Darts will handle them correctly. It will also complain if their time span is not sufficient.
- Parameters
series (
Union
[TimeSeries
,Sequence
[TimeSeries
]]) – A series or sequence of series serving as target (i.e. what the model will be trained to forecast)past_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, a series or sequence of series specifying past-observed covariatesfuture_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, a series or sequence of series specifying future-known covariatesval_series (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, one or a sequence of validation target series, which will be used to compute the validation loss throughout training and keep track of the best performing models.val_past_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, the past covariates corresponding to the validation series (must matchcovariates
)val_future_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, the future covariates corresponding to the validation series (must matchcovariates
)trainer (
Optional
[Trainer
]) – Optionally, a custom PyTorch-Lightning Trainer object to perform training. Using a customtrainer
will override Darts’ default trainer.verbose (
Optional
[bool
]) – Optionally, whether to print progress.epochs (
int
) – If specified, will train the model forepochs
(additional) epochs, irrespective of whatn_epochs
was provided to the model constructor.max_samples_per_ts (
Optional
[int
]) – Optionally, a maximum number of samples to use per time series. Models are trained in a supervised fashion by constructing slices of (input, output) examples. On long time series, this can result in unnecessarily large number of training samples. This parameter upper-bounds the number of training samples per time series (taking only the most recent samples in each series). Leaving to None does not apply any upper bound.num_loader_workers (
int
) – Optionally, an integer specifying thenum_workers
to use in PyTorchDataLoader
instances, both for the training and validation loaders (if any). A larger number of workers can sometimes increase performance, but can also incur extra overheads and increase memory usage, as more batches are loaded in parallel.
- Returns
Fitted model.
- Return type
self
- fit_from_dataset(train_dataset, val_dataset=None, trainer=None, verbose=None, epochs=0, num_loader_workers=0)¶
Train the model with a specific
darts.utils.data.TrainingDataset
instance. These datasets implement a PyTorchDataset
, and specify how the target and covariates are sliced for training. If you are not sure which training dataset to use, consider callingfit()
instead, which will create a default training dataset appropriate for this model.Training is performed with a PyTorch Lightning Trainer. It uses a default Trainer object from presets and
pl_trainer_kwargs
used at model creation. You can also use a custom Trainer with optional parametertrainer
. For more information on PyTorch Lightning Trainers check out this link.This function can be called several times to do some extra training. If
epochs
is specified, the model will be trained for some (extra)epochs
epochs.- Parameters
train_dataset (
TrainingDataset
) – A training dataset with a type matching this model (e.g.PastCovariatesTrainingDataset
forPastCovariatesTorchModel
).val_dataset (
Optional
[TrainingDataset
]) – A training dataset with a type matching this model (e.g.PastCovariatesTrainingDataset
for :class:`PastCovariatesTorchModel`s), representing the validation set (to track the validation loss).trainer (
Optional
[Trainer
]) – Optionally, a custom PyTorch-Lightning Trainer object to perform prediction. Using a custom trainer will override Darts’ default trainer.verbose (
Optional
[bool
]) – Optionally, whether to print progress.epochs (
int
) – If specified, will train the model forepochs
(additional) epochs, irrespective of whatn_epochs
was provided to the model constructor.num_loader_workers (
int
) – Optionally, an integer specifying thenum_workers
to use in PyTorchDataLoader
instances, both for the training and validation loaders (if any). A larger number of workers can sometimes increase performance, but can also incur extra overheads and increase memory usage, as more batches are loaded in parallel.
- Returns
Fitted model.
- Return type
self
- generate_fit_encodings(series, past_covariates=None, future_covariates=None)¶
Generates the covariate encodings that were used/generated for fitting the model and returns a tuple of past, and future covariates series with the original and encoded covariates stacked together. The encodings are generated by the encoders defined at model creation with parameter add_encoders. Pass the same series, past_covariates, and future_covariates that you used to train/fit the model.
- Parameters
series (
Union
[TimeSeries
,Sequence
[TimeSeries
]]) – The series or sequence of series with the target values used when fitting the model.past_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, the series or sequence of series with the past-observed covariates used when fitting the model.future_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, the series or sequence of series with the future-known covariates used when fitting the model.
- Returns
A tuple of (past covariates, future covariates). Each covariate contains the original as well as the encoded covariates.
- Return type
Tuple[Union[TimeSeries, Sequence[TimeSeries]], Union[TimeSeries, Sequence[TimeSeries]]]
- generate_fit_predict_encodings(n, series, past_covariates=None, future_covariates=None)¶
Generates covariate encodings for training and inference/prediction and returns a tuple of past, and future covariates series with the original and encoded covariates stacked together. The encodings are generated by the encoders defined at model creation with parameter add_encoders. Pass the same series, past_covariates, and future_covariates that you intend to use for training and prediction.
- Parameters
n (
int
) – The number of prediction time steps after the end of series intended to be used for prediction.series (
Union
[TimeSeries
,Sequence
[TimeSeries
]]) – The series or sequence of series with target values intended to be used for training and prediction.past_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, the past-observed covariates series intended to be used for training and prediction. The dimensions must match those of the covariates used for training.future_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, the future-known covariates series intended to be used for prediction. The dimensions must match those of the covariates used for training.
- Returns
A tuple of (past covariates, future covariates). Each covariate contains the original as well as the encoded covariates.
- Return type
Tuple[Union[TimeSeries, Sequence[TimeSeries]], Union[TimeSeries, Sequence[TimeSeries]]]
- generate_predict_encodings(n, series, past_covariates=None, future_covariates=None)¶
Generates covariate encodings for the inference/prediction set and returns a tuple of past, and future covariates series with the original and encoded covariates stacked together. The encodings are generated by the encoders defined at model creation with parameter add_encoders. Pass the same series, past_covariates, and future_covariates that you intend to use for prediction.
- Parameters
n (
int
) – The number of prediction time steps after the end of series intended to be used for prediction.series (
Union
[TimeSeries
,Sequence
[TimeSeries
]]) – The series or sequence of series with target values intended to be used for prediction.past_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, the past-observed covariates series intended to be used for prediction. The dimensions must match those of the covariates used for training.future_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, the future-known covariates series intended to be used for prediction. The dimensions must match those of the covariates used for training.
- Returns
A tuple of (past covariates, future covariates). Each covariate contains the original as well as the encoded covariates.
- Return type
Tuple[Union[TimeSeries, Sequence[TimeSeries]], Union[TimeSeries, Sequence[TimeSeries]]]
- classmethod gridsearch(parameters, series, past_covariates=None, future_covariates=None, forecast_horizon=None, stride=1, start=0.5, start_format='value', last_points_only=False, show_warnings=True, val_series=None, use_fitted_values=False, metric=<function mape>, reduction=<function mean>, verbose=False, n_jobs=1, n_random_samples=None)¶
Find the best hyper-parameters among a given set using a grid search.
This function has 3 modes of operation: Expanding window mode, split mode and fitted value mode. The three modes of operation evaluate every possible combination of hyper-parameter values provided in the parameters dictionary by instantiating the model_class subclass of ForecastingModel with each combination, and returning the best-performing model with regard to the metric function. The metric function is expected to return an error value, thus the model resulting in the smallest metric output will be chosen.
The relationship of the training data and test data depends on the mode of operation.
Expanding window mode (activated when forecast_horizon is passed): For every hyperparameter combination, the model is repeatedly trained and evaluated on different splits of series. This process is accomplished by using the
backtest()
function as a subroutine to produce historic forecasts starting from start that are compared against the ground truth values of series. Note that the model is retrained for every single prediction, thus this mode is slower.Split window mode (activated when val_series is passed): This mode will be used when the val_series argument is passed. For every hyper-parameter combination, the model is trained on series and evaluated on val_series.
Fitted value mode (activated when use_fitted_values is set to True): For every hyper-parameter combination, the model is trained on series and evaluated on the resulting fitted values. Not all models have fitted values, and this method raises an error if the model doesn’t have a fitted_values member. The fitted values are the result of the fit of the model on series. Comparing with the fitted values can be a quick way to assess the model, but one cannot see if the model is overfitting the series.
Derived classes must ensure that a single instance of a model will not share parameters with the other instances, e.g., saving models in the same path. Otherwise, an unexpected behavior can arise while running several models in parallel (when
n_jobs != 1
). If this cannot be avoided, then gridsearch should be redefined, forcingn_jobs = 1
.Currently this method only supports deterministic predictions (i.e. when models’ predictions have only 1 sample).
- Parameters
model_class – The ForecastingModel subclass to be tuned for ‘series’.
parameters (
dict
) – A dictionary containing as keys hyperparameter names, and as values lists of values for the respective hyperparameter.series (
TimeSeries
) – The target series used as input and target for training.past_covariates (
Optional
[TimeSeries
]) – Optionally, a past-observed covariate series. This applies only if the model supports past covariates.future_covariates (
Optional
[TimeSeries
]) – Optionally, a future-known covariate series. This applies only if the model supports future covariates.forecast_horizon (
Optional
[int
]) – The integer value of the forecasting horizon. Activates expanding window mode.stride (
int
) – Only used in expanding window mode. The number of time steps between two consecutive predictions.start (
Union
[Timestamp
,float
,int
]) –Only used in expanding window mode. Optionally, the first point in time at which a prediction is computed. This parameter supports:
float
,int
,pandas.Timestamp
, andNone
. If afloat
, it is the proportion of the time series that should lie before the first prediction point. If anint
, it is either the index position of the first prediction point for series with a pd.DatetimeIndex, or the index value for series with a pd.RangeIndex. The latter can be changed to the index position with start_format=”position”. If apandas.Timestamp
, it is the time stamp of the first prediction point. IfNone
, the first prediction point will automatically be set to:the first predictable point if retrain is
False
, or retrain is a Callable and the first predictable point is earlier than the first trainable point.the first trainable point if retrain is
True
orint
(given train_length), or retrain is a Callable and the first trainable point is earlier than the first predictable point.the first trainable point (given train_length) otherwise
Note: Raises a ValueError if start yields a time outside the time index of series. Note: If start is outside the possible historical forecasting times, will ignore the parameter (default behavior with
None
) and start at the first trainable/predictable point.start_format (
Literal
[‘position’, ‘value’]) – Only used in expanding window mode. Defines the start format. Only effective when start is an integer and series is indexed with a pd.RangeIndex. If set to ‘position’, start corresponds to the index position of the first predicted point and can range from (-len(series), len(series) - 1). If set to ‘value’, start corresponds to the index value/label of the first predicted point. Will raise an error if the value is not in series’ index. Default:'value'
last_points_only (
bool
) – Only used in expanding window mode. Whether to use the whole forecasts or only the last point of each forecast to compute the error.show_warnings (
bool
) – Only used in expanding window mode. Whether to show warnings related to the start parameter.val_series (
Optional
[TimeSeries
]) – The TimeSeries instance used for validation in split mode. If provided, this series must start right after the end of series; so that a proper comparison of the forecast can be made.use_fitted_values (
bool
) – If True, uses the comparison with the fitted values. Raises an error iffitted_values
is not an attribute of model_class.metric (
Callable
[[TimeSeries
,TimeSeries
],float
]) – A function that takes two TimeSeries instances as inputs (actual and prediction, in this order), and returns a float error value.reduction (
Callable
[[ndarray
],float
]) – A reduction function (mapping array to float) describing how to aggregate the errors obtained on the different validation series when backtesting. By default it’ll compute the mean of errors.verbose – Whether to print progress.
n_jobs (
int
) – The number of jobs to run in parallel. Parallel jobs are created only when there are two or more parameters combinations to evaluate. Each job will instantiate, train, and evaluate a different instance of the model. Defaults to 1 (sequential). Setting the parameter to -1 means using all the available cores.n_random_samples (
Union
[int
,float
,None
]) – The number/ratio of hyperparameter combinations to select from the full parameter grid. This will perform a random search instead of using the full grid. If an integer, n_random_samples is the number of parameter combinations selected from the full grid and must be between 0 and the total number of parameter combinations. If a float, n_random_samples is the ratio of parameter combinations selected from the full grid and must be between 0 and 1. Defaults to None, for which random selection will be ignored.
- Returns
A tuple containing an untrained model_class instance created from the best-performing hyper-parameters, along with a dictionary containing these best hyper-parameters, and metric score for the best hyper-parameters.
- Return type
ForecastingModel, Dict, float
- historical_forecasts(series, past_covariates=None, future_covariates=None, num_samples=1, train_length=None, start=None, start_format='value', forecast_horizon=1, stride=1, retrain=True, overlap_end=False, last_points_only=True, verbose=False, show_warnings=True, predict_likelihood_parameters=False, enable_optimization=True)¶
Compute the historical forecasts that would have been obtained by this model on (potentially multiple) series.
This method repeatedly builds a training set: either expanding from the beginning of series or moving with a fixed length train_length. It trains the model on the training set, emits a forecast of length equal to forecast_horizon, and then moves the end of the training set forward by stride time steps.
By default, this method will return one (or a sequence of) single time series made up of the last point of each historical forecast. This time series will thus have a frequency of
series.freq * stride
. If last_points_only is set to False, it will instead return one (or a sequence of) list of the historical forecasts series.By default, this method always re-trains the models on the entire available history, corresponding to an expanding window strategy. If retrain is set to False, the model must have been fit before. This is not supported by all models.
- Parameters
series (
Union
[TimeSeries
,Sequence
[TimeSeries
]]) – The (or a sequence of) target time series used to successively train and compute the historical forecasts.past_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, one (or a sequence of) past-observed covariate series. This applies only if the model supports past covariates.future_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, one (or a sequence of) of future-known covariate series. This applies only if the model supports future covariates.num_samples (
int
) – Number of times a prediction is sampled from a probabilistic model. Use values >1 only for probabilistic models.train_length (
Optional
[int
]) – Number of time steps in our training set (size of backtesting window to train on). Only effective when retrain is notFalse
. Default is set to train_length=None where it takes all available time steps up until prediction time, otherwise the moving window strategy is used. If larger than the number of time steps available, all steps up until prediction time are used, as in default case. Needs to be at least min_train_series_length.start (
Union
[Timestamp
,float
,int
,None
]) –Optionally, the first point in time at which a prediction is computed. This parameter supports:
float
,int
,pandas.Timestamp
, andNone
. If afloat
, it is the proportion of the time series that should lie before the first prediction point. If anint
, it is either the index position of the first prediction point for series with a pd.DatetimeIndex, or the index value for series with a pd.RangeIndex. The latter can be changed to the index position with start_format=”position”. If apandas.Timestamp
, it is the time stamp of the first prediction point. IfNone
, the first prediction point will automatically be set to:the first predictable point if retrain is
False
, or retrain is a Callable and the first predictable point is earlier than the first trainable point.the first trainable point if retrain is
True
orint
(given train_length), or retrain is a Callable and the first trainable point is earlier than the first predictable point.the first trainable point (given train_length) otherwise
Note: Raises a ValueError if start yields a time outside the time index of series. Note: If start is outside the possible historical forecasting times, will ignore the parameter (default behavior with
None
) and start at the first trainable/predictable point.start_format (
Literal
[‘position’, ‘value’]) – Defines the start format. Only effective when start is an integer and series is indexed with a pd.RangeIndex. If set to ‘position’, start corresponds to the index position of the first predicted point and can range from (-len(series), len(series) - 1). If set to ‘value’, start corresponds to the index value/label of the first predicted point. Will raise an error if the value is not in series’ index. Default:'value'
forecast_horizon (
int
) – The forecast horizon for the predictions.stride (
int
) – The number of time steps between two consecutive predictions.retrain (
Union
[bool
,int
,Callable
[…,bool
]]) –Whether and/or on which condition to retrain the model before predicting. This parameter supports 3 different datatypes:
bool
, (positive)int
, andCallable
(returning abool
). In the case ofbool
: retrain the model at each step (True), or never retrains the model (False). In the case ofint
: the model is retrained every retrain iterations. In the case ofCallable
: the model is retrained whenever callable returns True. The callable must have the following positional arguments:counter (int): current retrain iteration
pred_time (pd.Timestamp or int): timestamp of forecast time (end of the training series)
train_series (TimeSeries): train series up to pred_time
past_covariates (TimeSeries): past_covariates series up to pred_time
future_covariates (TimeSeries): future_covariates series up to min(pred_time + series.freq * forecast_horizon, series.end_time())
Note: if any optional *_covariates are not passed to historical_forecast,
None
will be passed to the corresponding retrain function argument. Note: some models do require being retrained every time and do not support anything other than retrain=True.overlap_end (
bool
) – Whether the returned forecasts can go beyond the series’ end or not.last_points_only (
bool
) – Whether to retain only the last point of each historical forecast. If set to True, the method returns a singleTimeSeries
containing the successive point forecasts. Otherwise, returns a list of historicalTimeSeries
forecasts.verbose (
bool
) – Whether to print progress.show_warnings (
bool
) – Whether to show warnings related to historical forecasts optimization, or parameters start and train_length.predict_likelihood_parameters (
bool
) – If set to True, the model predict the parameters of its Likelihood parameters instead of the target. Only supported for probabilistic models with a likelihood, num_samples = 1 and n<=output_chunk_length. Default:False
enable_optimization (
bool
) – Whether to use the optimized version of historical_forecasts when supported and available.
- Returns
If last_points_only is set to True and a single series is provided in input, a single
TimeSeries
is returned, which contains the historical forecast at the desired horizon.A
List[TimeSeries]
is returned if either series is aSequence
ofTimeSeries
, or if last_points_only is set to False. A list of lists is returned if both conditions are met. In this last case, the outer list is over the series provided in the input sequence, and the inner lists contain the different historical forecasts.- Return type
TimeSeries or List[TimeSeries] or List[List[TimeSeries]]
- property input_chunk_length: int¶
- Return type
int
- property likelihood: Optional[darts.utils.likelihood_models.Likelihood]¶
- Return type
Optional
[Likelihood
]
- static load(path, **kwargs)¶
Loads a model from a given file path.
Example for loading a general save from
RNNModel
:from darts.models import RNNModel model_loaded = RNNModel.load(path)
Example for loading an
RNNModel
to CPU that was saved on GPU:from darts.models import RNNModel model_loaded = RNNModel.load(path, map_location="cpu") model_loaded.to_cpu()
- Parameters
path (
str
) – Path from which to load the model. If no path was specified when saving the model, the automatically generated path ending with “.pt” has to be provided.**kwargs – Additional kwargs for PyTorch Lightning’s
LightningModule.load_from_checkpoint()
method, such asmap_location
to load the model onto a different device than the one from which it was saved. For more information, read the official documentation.
- Return type
TorchForecastingModel
- static load_from_checkpoint(model_name, work_dir=None, file_name=None, best=True, **kwargs)¶
Load the model from automatically saved checkpoints under ‘{work_dir}/darts_logs/{model_name}/checkpoints/’. This method is used for models that were created with
save_checkpoints=True
.If you manually saved your model, consider using
load()
.Example for loading a
RNNModel
from checkpoint (model_name
is themodel_name
used at model creation):from darts.models import RNNModel model_loaded = RNNModel.load_from_checkpoint(model_name, best=True)
If
file_name
is given, returns the model saved under ‘{work_dir}/darts_logs/{model_name}/checkpoints/{file_name}’.If
file_name
is not given, will try to restore the best checkpoint (ifbest
isTrue
) or the most recent checkpoint (ifbest
isFalse
from ‘{work_dir}/darts_logs/{model_name}/checkpoints/’.Example for loading an
RNNModel
checkpoint to CPU that was saved on GPU:from darts.models import RNNModel model_loaded = RNNModel.load_from_checkpoint(model_name, best=True, map_location="cpu") model_loaded.to_cpu()
- Parameters
model_name (
str
) – The name of the model, used to retrieve the checkpoints folder’s name.work_dir (
Optional
[str
]) – Working directory (containing the checkpoints folder). Defaults to current working directory.file_name (
Optional
[str
]) – The name of the checkpoint file. If not specified, use the most recent one.best (
bool
) – If set, will retrieve the best model (according to validation loss) instead of the most recent one. Only is ignored whenfile_name
is given.**kwargs –
Additional kwargs for PyTorch Lightning’s
LightningModule.load_from_checkpoint()
method, such asmap_location
to load the model onto a different device than the one from which it was saved. For more information, read the official documentation.
- Returns
The corresponding trained
TorchForecastingModel
.- Return type
TorchForecastingModel
- load_weights(path, load_encoders=True, skip_checks=False, **kwargs)¶
Loads the weights from a manually saved model (saved with
save()
).Note: This method needs to be able to access the darts model checkpoint (.pt) in order to load the encoders and perform sanity checks on the model parameters.
- Parameters
path (
str
) – Path from which to load the model’s weights. If no path was specified when saving the model, the automatically generated path ending with “.pt” has to be provided.load_encoders (
bool
) – If set, will load the encoders from the model to enable direct call of fit() or predict(). Default:True
.skip_checks (
bool
) – If set, will disable the loading of the encoders and the sanity checks on model parameters (not recommended). Cannot be used with load_encoders=True. Default:False
.**kwargs –
Additional kwargs for PyTorch’s
load()
method, such asmap_location
to load the model onto a different device than the one from which it was saved. For more information, read the official documentation.
- load_weights_from_checkpoint(model_name=None, work_dir=None, file_name=None, best=True, strict=True, load_encoders=True, skip_checks=False, **kwargs)¶
Load only the weights from automatically saved checkpoints under ‘{work_dir}/darts_logs/{model_name}/ checkpoints/’. This method is used for models that were created with
save_checkpoints=True
and that need to be re-trained or fine-tuned with different optimizer or learning rate scheduler. However, it can also be used to load weights for inference.To resume an interrupted training, please consider using
load_from_checkpoint()
which also reload the trainer, optimizer and learning rate scheduler states.For manually saved model, consider using
load()
orload_weights()
instead.Note: This method needs to be able to access the darts model checkpoint (.pt) in order to load the encoders and perform sanity checks on the model parameters.
- Parameters
model_name (
Optional
[str
]) – The name of the model, used to retrieve the checkpoints folder’s name. Default:self.model_name
.work_dir (
Optional
[str
]) – Working directory (containing the checkpoints folder). Defaults to current working directory.file_name (
Optional
[str
]) – The name of the checkpoint file. If not specified, use the most recent one.best (
bool
) – If set, will retrieve the best model (according to validation loss) instead of the most recent one. Only is ignored whenfile_name
is given. Default:True
.strict (
bool
) –If set, strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict(). Default:
True
. For more information, read the official documentation.load_encoders (
bool
) – If set, will load the encoders from the model to enable direct call of fit() or predict(). Default:True
.skip_checks (
bool
) – If set, will disable the loading of the encoders and the sanity checks on model parameters (not recommended). Cannot be used with load_encoders=True. Default:False
.**kwargs –
Additional kwargs for PyTorch’s
load()
method, such asmap_location
to load the model onto a different device than the one from which it was saved. For more information, read the official documentation.
- lr_find(series, past_covariates=None, future_covariates=None, val_series=None, val_past_covariates=None, val_future_covariates=None, trainer=None, verbose=None, epochs=0, max_samples_per_ts=None, num_loader_workers=0, min_lr=1e-08, max_lr=1, num_training=100, mode='exponential', early_stop_threshold=4.0)¶
A wrapper around PyTorch Lightning’s Tuner.lr_find(). Performs a range test of good initial learning rates, to reduce the amount of guesswork in picking a good starting learning rate. For more information on PyTorch Lightning’s Tuner check out this link. It is recommended to increase the number of epochs if the tuner did not give satisfactory results. Consider creating a new model object with the suggested learning rate for example using model creation parameters optimizer_cls, optimizer_kwargs, lr_scheduler_cls, and lr_scheduler_kwargs.
Example using a
RNNModel
:import torch from darts.datasets import AirPassengersDataset from darts.models import NBEATSModel series = AirPassengersDataset().load() train, val = series[:-18], series[-18:] model = NBEATSModel(input_chunk_length=12, output_chunk_length=6, random_state=42) # run the learning rate tuner results = model.lr_find(series=train, val_series=val) # plot the results results.plot(suggest=True, show=True) # create a new model with the suggested learning rate model = NBEATSModel( input_chunk_length=12, output_chunk_length=6, random_state=42, optimizer_cls=torch.optim.Adam, optimizer_kwargs={"lr": results.suggestion()} )
- Parameters
series (
Union
[TimeSeries
,Sequence
[TimeSeries
]]) – A series or sequence of series serving as target (i.e. what the model will be trained to forecast)past_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, a series or sequence of series specifying past-observed covariatesfuture_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, a series or sequence of series specifying future-known covariatesval_series (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, one or a sequence of validation target series, which will be used to compute the validation loss throughout training and keep track of the best performing models.val_past_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, the past covariates corresponding to the validation series (must matchcovariates
)val_future_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, the future covariates corresponding to the validation series (must matchcovariates
)trainer (
Optional
[Trainer
]) – Optionally, a custom PyTorch-Lightning Trainer object to perform training. Using a customtrainer
will override Darts’ default trainer.verbose (
Optional
[bool
]) – Optionally, whether to print progress.epochs (
int
) – If specified, will train the model forepochs
(additional) epochs, irrespective of whatn_epochs
was provided to the model constructor.max_samples_per_ts (
Optional
[int
]) – Optionally, a maximum number of samples to use per time series. Models are trained in a supervised fashion by constructing slices of (input, output) examples. On long time series, this can result in unnecessarily large number of training samples. This parameter upper-bounds the number of training samples per time series (taking only the most recent samples in each series). Leaving to None does not apply any upper bound.num_loader_workers (
int
) – Optionally, an integer specifying thenum_workers
to use in PyTorchDataLoader
instances, both for the training and validation loaders (if any). A larger number of workers can sometimes increase performance, but can also incur extra overheads and increase memory usage, as more batches are loaded in parallel.min_lr (
float
) – minimum learning rate to investigatemax_lr (
float
) – maximum learning rate to investigatenum_training (
int
) – number of learning rates to testmode (
str
) – Search strategy to update learning rate after each batch: ‘exponential’: Increases the learning rate exponentially. ‘linear’: Increases the learning rate linearly.early_stop_threshold (
float
) – Threshold for stopping the search. If the loss at any point is larger than early_stop_threshold*best_loss then the search is stopped. To disable, set to None
- Returns
_LRFinder object of Lightning containing the results of the LR sweep.
- Return type
lr_finder
- property min_train_samples: int¶
The minimum number of samples for training the model.
- Return type
int
- property model_created: bool¶
- Return type
bool
- property model_params: dict¶
- Return type
dict
- property output_chunk_length: int¶
Number of time steps predicted at once by the model, not defined for statistical models.
- Return type
int
- predict(n, series=None, past_covariates=None, future_covariates=None, trainer=None, batch_size=None, verbose=None, n_jobs=1, roll_size=None, num_samples=1, num_loader_workers=0, mc_dropout=False, predict_likelihood_parameters=False)¶
Predict the
n
time step following the end of the training series, or of the specifiedseries
.Prediction is performed with a PyTorch Lightning Trainer. It uses a default Trainer object from presets and
pl_trainer_kwargs
used at model creation. You can also use a custom Trainer with optional parametertrainer
. For more information on PyTorch Lightning Trainers check out this link .Below, all possible parameters are documented, but not all models support all parameters. For instance, all the
PastCovariatesTorchModel
support onlypast_covariates
and notfuture_covariates
. Darts will complain if you try callingpredict()
on a model with the wrong covariates argument.Darts will also complain if the provided covariates do not have a sufficient time span. In general, not all models require the same covariates’ time spans:
- Models relying on past covariates require the last
input_chunk_length
of thepast_covariates
points to be known at prediction time. For horizon valuesn > output_chunk_length
, these modelsrequire at least the nextn - output_chunk_length
future values to be known as well. - Models relying on future covariates require the next
n
values to be known.In addition (forDualCovariatesTorchModel
andMixedCovariatesTorchModel
), they alsorequire the “historic” values of these future covariates (over the pastinput_chunk_length
).
When handling covariates, Darts will try to use the time axes of the target and the covariates to come up with the right time slices. So the covariates can be longer than needed; as long as the time axes are correct Darts will handle them correctly. It will also complain if their time span is not sufficient.
- Parameters
n (
int
) – The number of time steps after the end of the training time series for which to produce predictionsseries (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, a series or sequence of series, representing the history of the target series whose future is to be predicted. If specified, the method returns the forecasts of these series. Otherwise, the method returns the forecast of the (single) training series.past_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, the past-observed covariates series needed as inputs for the model. They must match the covariates used for training in terms of dimension.future_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – Optionally, the future-known covariates series needed as inputs for the model. They must match the covariates used for training in terms of dimension.trainer (
Optional
[Trainer
]) – Optionally, a custom PyTorch-Lightning Trainer object to perform prediction. Using a customtrainer
will override Darts’ default trainer.batch_size (
Optional
[int
]) – Size of batches during prediction. Defaults to the models’ trainingbatch_size
value.verbose (
Optional
[bool
]) – Optionally, whether to print progress.n_jobs (
int
) – The number of jobs to run in parallel.-1
means using all processors. Defaults to1
.roll_size (
Optional
[int
]) – For self-consuming predictions, i.e.n > output_chunk_length
, determines how many outputs of the model are fed back into it at every iteration of feeding the predicted target (and optionally future covariates) back into the model. If this parameter is not provided, it will be setoutput_chunk_length
by default.num_samples (
int
) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 for deterministic models.num_loader_workers (
int
) – Optionally, an integer specifying thenum_workers
to use in PyTorchDataLoader
instances, for the inference/prediction dataset loaders (if any). A larger number of workers can sometimes increase performance, but can also incur extra overheads and increase memory usage, as more batches are loaded in parallel.mc_dropout (
bool
) – Optionally, enable monte carlo dropout for predictions using neural network based models. This allows bayesian approximation by specifying an implicit prior over learned models.predict_likelihood_parameters (
bool
) – If set to True, the model predict the parameters of its Likelihood parameters instead of the target. Only supported for probabilistic models with a likelihood, num_samples = 1 and n<=output_chunk_length. Default:False
- Returns
One or several time series containing the forecasts of
series
, or the forecast of the training series ifseries
is not specified and the model has been trained on a single series.- Return type
Union[TimeSeries, Sequence[TimeSeries]]
- predict_from_dataset(n, input_series_dataset, trainer=None, batch_size=None, verbose=None, n_jobs=1, roll_size=None, num_samples=1, num_loader_workers=0, mc_dropout=False, predict_likelihood_parameters=False)¶
This method allows for predicting with a specific
darts.utils.data.InferenceDataset
instance. These datasets implement a PyTorchDataset
, and specify how the target and covariates are sliced for inference. In most cases, you’ll rather want to callpredict()
instead, which will create an appropriateInferenceDataset
for you.Prediction is performed with a PyTorch Lightning Trainer. It uses a default Trainer object from presets and
pl_trainer_kwargs
used at model creation. You can also use a custom Trainer with optional parametertrainer
. For more information on PyTorch Lightning Trainers check out this link .- Parameters
n (
int
) – The number of time steps after the end of the training time series for which to produce predictionsinput_series_dataset (
InferenceDataset
) – Optionally, a series or sequence of series, representing the history of the target series’ whose future is to be predicted. If specified, the method returns the forecasts of these series. Otherwise, the method returns the forecast of the (single) training series.trainer (
Optional
[Trainer
]) – Optionally, a custom PyTorch-Lightning Trainer object to perform prediction. Using a customtrainer
will override Darts’ default trainer.batch_size (
Optional
[int
]) – Size of batches during prediction. Defaults to the modelsbatch_size
value.verbose (
Optional
[bool
]) – Optionally, whether to print progress.n_jobs (
int
) – The number of jobs to run in parallel.-1
means using all processors. Defaults to1
.roll_size (
Optional
[int
]) – For self-consuming predictions, i.e.n > output_chunk_length
, determines how many outputs of the model are fed back into it at every iteration of feeding the predicted target (and optionally future covariates) back into the model. If this parameter is not provided, it will be setoutput_chunk_length
by default.num_samples (
int
) – Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 for deterministic models.num_loader_workers (
int
) – Optionally, an integer specifying thenum_workers
to use in PyTorchDataLoader
instances, for the inference/prediction dataset loaders (if any). A larger number of workers can sometimes increase performance, but can also incur extra overheads and increase memory usage, as more batches are loaded in parallel.mc_dropout (
bool
) – Optionally, enable monte carlo dropout for predictions using neural network based models. This allows bayesian approximation by specifying an implicit prior over learned models.predict_likelihood_parameters (
bool
) – If set to True, the model predict the parameters of its Likelihood parameters instead of the target. Only supported for probabilistic models with a likelihood, num_samples = 1 and n<=output_chunk_length. Default:False
- Returns
Returns one or more forecasts for time series.
- Return type
Sequence[TimeSeries]
- reset_model()¶
Resets the model object and removes all stored data - model, checkpoints, loggers and training history.
- residuals(series, past_covariates=None, future_covariates=None, forecast_horizon=1, retrain=True, verbose=False)¶
Compute the residuals produced by this model on a (or sequence of) univariate time series.
This function computes the difference between the actual observations from series and the fitted values vector p obtained by training the model on series. For every index i in series, p[i] is computed by training the model on
series[:(i - forecast_horizon)]
and forecasting forecast_horizon into the future. (p[i] will be set to the last value of the predicted series.) The vector of residuals will be shorter than series due to the minimum training series length required by the model and the gap introduced by forecast_horizon. Most commonly, the term “residuals” implies a value for forecast_horizon of 1; but this can be configured.This method works only on univariate series. It uses the median prediction (when dealing with stochastic forecasts).
- Parameters
series (
Union
[TimeSeries
,Sequence
[TimeSeries
]]) – The univariate TimeSeries instance which the residuals will be computed for.past_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – One or several past-observed covariate time series.future_covariates (
Union
[TimeSeries
,Sequence
[TimeSeries
],None
]) – One or several future-known covariate time series.forecast_horizon (
int
) – The forecasting horizon used to predict each fitted value.retrain (
bool
) – Whether to train the model at each iteration, for models that support it. If False, the model is not trained at all. Default: Trueverbose (
bool
) – Whether to print progress.
- Returns
The vector of residuals.
- Return type
TimeSeries (or Sequence[TimeSeries])
- save(path=None)¶
Saves the model under a given path.
Creates two files under
path
(model object) andpath
.ckpt (checkpoint).Example for saving and loading a
RNNModel
:from darts.models import RNNModel model = RNNModel(input_chunk_length=4) model.save("my_model.pt") model_loaded = RNNModel.load("my_model.pt")
- Parameters
path (
Optional
[str
]) – Path under which to save the model at its current state. Please avoid path starting with “last-” or “best-” to avoid collision with Pytorch-Ligthning checkpoints. If no path is specified, the model is automatically saved under"{ModelClass}_{YYYY-mm-dd_HH_MM_SS}.pt"
. E.g.,"RNNModel_2020-01-01_12_00_00.pt"
.- Return type
None
- supports_future_covariates = False¶
- property supports_likelihood_parameter_prediction: bool¶
Whether model instance supports direct prediction of likelihood parameters
- Return type
bool
- property supports_multivariate: bool¶
Whether the model considers more than one variate in the time series.
- Return type
bool
- property supports_optimized_historical_forecasts: bool¶
Whether the model supports optimized historical forecasts
- Return type
bool
- property supports_past_covariates: bool¶
Whether model supports past covariates
- Return type
bool
- property supports_static_covariates: bool¶
Whether model supports static covariates
- Return type
bool
- to_cpu()¶
Updates the PyTorch Lightning Trainer parameters to move the model to CPU the next time :fun:`fit()` or
predict()
is called.
- property uses_future_covariates: bool¶
Whether the model uses future covariates, once fitted.
- Return type
bool
- property uses_past_covariates: bool¶
Whether the model uses past covariates, once fitted.
- Return type
bool
- property uses_static_covariates: bool¶
Whether the model uses static covariates, once fitted.
- Return type
bool