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

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title	description	keywords
trans	This section emphasizes on Transformer forecasting method implementation and visualization used in OpenBB Terminal. It explains functions like how to apply the approach on data, various parameters to adjust for better forecasting and display the results in chart.	Transformer Forecasting Time-Series Prediction Forecasting Parameters Data Visualization pandas series pandas dataframe Machine Learning Activation Function Multi-head Attention Mechanism Encoder layers Decoder layers Feedforward Network

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Performs Transformer forecasting

Source Code: [link]

openbb.forecast.trans(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, d_model: int = 64, nhead: int = 4, num_encoder_layers: int = 3, num_decoder_layers: int = 3, dim_feedforward: int = 512, activation: str = "relu", dropout: float = 0.0, batch_size: int = 32, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = "trans_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
n_predict	int	Days to predict. Defaults to 5.	5	True
target_column	str	Target column to forecast. Defaults to "close".	close	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
d_model	int	The number of expected features in the encoder/decoder inputs. Defaults to 64.	64	True
nhead	int	The number of heads in the multi-head attention mechanism. Defaults to 4.	4	True
num_encoder_layers	int	The number of encoder layers in the encoder. Defaults to 3.	3	True
num_decoder_layers	int	The number of decoder layers in the encoder. Defaults to 3.	3	True
dim_feedforward	int	The dimension of the feedforward network model. Defaults to 512.	512	True
activation	str	The activation function of encoder/decoder intermediate layer, ‘relu’ or ‘gelu’. Defaults to 'relu'.	relu	True
dropout	float	Fraction of neurons affected by Dropout. Defaults to 0.0.	0.0	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".	trans_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[TransformerModel]]	Adjusted Data series, Historical forecast by best RNN model, list of Predictions, Mean average precision error, Best transformer Model.

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Display Transformer forecast

Source Code: [link]

openbb.forecast.trans_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, d_model: int = 64, nhead: int = 4, num_encoder_layers: int = 3, num_decoder_layers: int = 3, dim_feedforward: int = 512, activation: str = "relu", dropout: float = 0.1, batch_size: int = 16, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = "trans_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
dataset_name	str	The name of the ticker to be predicted		True
n_predict	int	Days to predict. Defaults to 5.	5	True
target_column	str	Target column to forecast. Defaults to "close".	close	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
d_model	int	The number of expected features in the encoder/decoder inputs. Defaults to 64.	64	True
nhead	int	The number of heads in the multi-head attention mechanism. Defaults to 4.	4	True
num_encoder_layers	int	The number of encoder layers in the encoder. Defaults to 3.	3	True
num_decoder_layers	int	The number of decoder layers in the encoder. Defaults to 3.	3	True
dim_feedforward	int	The dimension of the feedforward network model. Defaults to 512.	512	True
activation	str	The activation function of encoder/decoder intermediate layer, ‘relu’ or ‘gelu’. Defaults to 'relu'.	relu	True
dropout	float	Fraction of neurons affected by Dropout. Defaults to 0.1.	0.1	True
batch_size_	int	Number of time series (input and output sequences) used in each training pass. Defaults to 32.	None	True
n_epochs	int	Number of epochs over which to train the model. Defaults to 100.	100	True
model_save_name	str	Name for model. Defaults to "brnn_model".	trans_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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