OpenBB/website/content/sdk/reference/forecast/nhits.md

title, description

title	description
nhits	OpenBB SDK Function

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nhits

Performs Nhits forecasting

Source Code: [link]

openbb.forecast.nhits(data: Union[pd.Series, pd.DataFrame], target_column: str = "close", n_predict: int = 5, train_split: float = 0.85, past_covariates: str = None, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, num_stacks: int = 3, num_blocks: int = 1, num_layers: int = 2, layer_widths: int = 512, pooling_kernel_sizes: Optional[Tuple[Tuple[int]]] = None, n_freq_downsample: Optional[Tuple[Tuple[int]]] = None, dropout: float = 0.1, activation: str = "ReLU", max_pool_1d: bool = True, batch_size: int = 32, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = "brnn_model", force_reset: bool = True, save_checkpoints: bool = True)

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Parameters

Name	Type	Description	Default	Optional
data	Union[pd.Series, pd.DataFrame]	Input Data	None	False
target_column	str	Target column to forecast. Defaults to "close".	close	True
n_predict	int	Days to predict. Defaults to 5.	5	True
train_split	float	Train/val split. Defaults to 0.85.	0.85	True
past_covariates	str	Multiple secondary columns to factor in when forecasting. Defaults to None.	None	True
forecast_horizon	int	Forecast horizon when performing historical forecasting. Defaults to 5.	5	True
input_chunk_length	int	Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14.	14	True
output_chunk_length	int	The length of the forecast of the model. Defaults to 5.	5	True
num_stacks	int	The number of stacks that make up the whole model.	3	True
num_blocks	int	The number of blocks making up every stack.	1	True
num_layers	int	The number of fully connected layers preceding the final forking layers in each block of every stack.	2	True
layer_widths	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.	512	True
pooling_kernel_size	Optional[Tuple[Tuple[int]]]:	If set, this parameter must be a tuple of tuples, of size (num_stacks x num_blocks), specifying the kernel size for each block in each stack used for the input pooling layer. If left to None, some default values will be used based on input_chunk_length.	None	True
n_freq_downsample	Optional[Tuple[Tuple[int]]]	If set, this parameter must be a tuple of tuples, of size (num_stacks x num_blocks), specifying the downsampling factors before interpolation, for each block in each stack. If left to None, some default values will be used based on output_chunk_length.	None	True
dropout	float	The dropout probability to be used in fully connected layers.	0.1	True
activation	str	Supported activations: [‘ReLU’,’RReLU’, ‘PReLU’, ‘Softplus’, ‘Tanh’, ‘SELU’, ‘LeakyReLU’, ‘Sigmoid’]	ReLU	True
max_pool_1d	bool	Use max_pool_1d pooling. False uses AvgPool1d.	True	True
batch_size	int	Number of time series (input and output sequences) used in each training pass. Defaults to 32.	32	True
n_epochs	int	Number of epochs over which to train the model. Defaults to 100.	100	True
learning_rate	float	Defaults to 1e-3.	0.001	True
model_save_name	str	Name for model. Defaults to "brnn_model".	brnn_model	True
force_reset	bool	If set to True, any previously-existing model with the same name will be reset (all checkpoints will be discarded). Defaults to True.	True	True
save_checkpoints	bool	Whether or not to automatically save the untrained model and checkpoints from training. Defaults to True.	True	True

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Returns

Type	Description
Tuple[List[TimeSeries], List[TimeSeries], List[TimeSeries], Optional[float], type[NHiTSModel]]	Adjusted Data series, Historical forecast by best RNN model, list of Predictions, Mean average precision error, Best BRNN Model.

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Display Nhits forecast

Source Code: [link]

openbb.forecast.nhits_chart(data: Union[pd.Series, pd.DataFrame], target_column: str = "close", dataset_name: str = "", n_predict: int = 5, past_covariates: str = None, train_split: float = 0.85, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, num_stacks: int = 3, num_blocks: int = 1, num_layers: int = 2, layer_widths: int = 512, pooling_kernel_sizes: Optional[Tuple[Tuple[int]]] = None, n_freq_downsample: Optional[Tuple[Tuple[int]]] = None, dropout: float = 0.1, activation: str = "ReLU", max_pool_1d: bool = True, batch_size: int = 32, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = "rnn_model", force_reset: bool = True, save_checkpoints: bool = True, export: str = "", residuals: bool = False, forecast_only: bool = False, start_date: Optional[datetime.datetime] = None, end_date: Optional[datetime.datetime] = None, naive: bool = False, export_pred_raw: bool = False, external_axes: Optional[List[axes]] = None)

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Parameters

Name	Type	Description	Default	Optional
data	Union[pd.Series, pd.DataFrame]	Input Data	None	False
target_column	str	Target column to forecast. Defaults to "close".	close	True
dataset_name	str	The name of the ticker to be predicted		True
n_predict	int	Days to predict. Defaults to 5.	5	True
train_split	float	Train/val split. Defaults to 0.85.	0.85	True
past_covariates	str	Multiple secondary columns to factor in when forecasting. Defaults to None.	None	True
forecast_horizon	int	Forecast horizon when performing historical forecasting. Defaults to 5.	5	True
input_chunk_length	int	Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14.	14	True
output_chunk_length	int	The length of the forecast of the model. Defaults to 5.	5	True
num_stacks	int	The number of stacks that make up the whole model.	3	True
num_blocks	int	The number of blocks making up every stack.	1	True
num_layers	int	The number of fully connected layers preceding the final forking layers in each block of every stack.	2	True
layer_widths	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.	512	True
pooling_kernel_size	Optional[Tuple[Tuple[int]]]	If set, this parameter must be a tuple of tuples, of size (num_stacks x num_blocks), specifying the kernel size for each block in each stack used for the input pooling layer. If left to None, some default values will be used based on input_chunk_length.	None	True
n_freq_downsample	Optional[Tuple[Tuple[int]]]	If set, this parameter must be a tuple of tuples, of size (num_stacks x num_blocks), specifying the downsampling factors before interpolation, for each block in each stack. If left to None, some default values will be used based on output_chunk_length.	None	True
dropout	float	The dropout probability to be used in fully connected layers.	0.1	True
activation	str	Supported activations: [[‘ReLU’,’RReLU’, ‘PReLU’, ‘Softplus’, ‘Tanh’, ‘SELU’, ‘LeakyReLU’, ‘Sigmoid’]	ReLU	True
max_pool_1d	bool	Use max_pool_1d pooling. False uses AvgPool1d.	True	True
batch_size	int	Number of time series (input and output sequences) used in each training pass. Defaults to 32.	32	True
n_epochs	int	Number of epochs over which to train the model. Defaults to 100.	100	True
learning_rate	float	Defaults to 1e-3.	0.001	True
model_save_name	str	Name for model. Defaults to "brnn_model".	rnn_model	True
force_reset	bool	If set to True, any previously-existing model with the same name will be reset (all checkpoints will be discarded). Defaults to True.	True	True
save_checkpoints	bool	Whether or not to automatically save the untrained model and checkpoints from training. Defaults to True.	True	True
export	str	Format to export data		True
residuals	bool	Whether to show residuals for the model. Defaults to False.	False	True
forecast_only	bool	Whether to only show dates in the forecasting range. Defaults to False.	False	True
start_date	Optional[datetime]	The starting date to perform analysis, data before this is trimmed. Defaults to None.	None	True
end_date	Optional[datetime]	The ending date to perform analysis, data after this is trimmed. Defaults to None.	None	True
naive	bool	Whether to show the naive baseline. This just assumes the closing price will be the same as the previous day's closing price. Defaults to False.	False	True
external_axes	Optional[List[plt.axes]]	External axes to plot on	None	True

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Returns

This function does not return anything

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