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

title, description

title	description
nhits	OpenBB Terminal Function

nhits

Perform nhits forecast: https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html

Usage

nhits [--num-stacks NUM_STACKS] [--num-blocks NUM_BLOCKS] [--num-layers NUM_LAYERS] [--layer_widths LAYER_WIDTHS] [--activation {ReLU,RReLU,PReLU,Softplus,Tanh,SELU,LeakyReLU,Sigmoid}] [--max_pool_1d] [--past-covariates PAST_COVARIATES] [--all-past-covariates] [--naive] [-d {}] [-c TARGET_COLUMN] [-n N_DAYS] [-t TRAIN_SPLIT] [-i INPUT_CHUNK_LENGTH] [-o OUTPUT_CHUNK_LENGTH] [--force-reset FORCE_RESET] [--save-checkpoints SAVE_CHECKPOINTS] [--model-save-name MODEL_SAVE_NAME] [--n-epochs N_EPOCHS] [--dropout DROPOUT] [--batch-size BATCH_SIZE] [--end S_END_DATE] [--start S_START_DATE] [--residuals] [--forecast-only] [--export-pred-raw]

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Parameters

Name	Description	Default	Optional	Choices
num_stacks	The number of stacks that make up the model	3	True	None
num_blocks	The number of blocks making up every stack	1	True	None
num_layers	The number of fully connected layers	2	True	None
layer_widths	The number of neurons in each layer	512	True	None
activation	The desired activation	ReLU	True	ReLU, RReLU, PReLU, Softplus, Tanh, SELU, LeakyReLU, Sigmoid
maxpool1d	Whether to use max_pool_1d or AvgPool1d	True	True	None
past_covariates	Past covariates(columns/features) in same dataset. Comma separated.	None	True	None
all_past_covariates	Adds all rows as past covariates except for date and the target column.	False	True	None
naive	Show the naive baseline for a model.	False	True	None
target_dataset	The name of the dataset you want to select	None	True	None
target_column	The name of the specific column you want to use	close	True	None
n_days	prediction days.	5	True	None
train_split	Start point for rolling training and forecast window. 0.0-1.0	0.85	True	None
input_chunk_length	Number of past time steps for forecasting module at prediction time.	14	True	None
output_chunk_length	The length of the forecast of the model.	5	True	None
force_reset	If set to True, any previously-existing model with the same name will be reset (all checkpoints will be discarded).	True	True	None
save_checkpoints	Whether to automatically save the untrained model and checkpoints.	True	True	None
model_save_name	Name of the model to save.	nhits_model	True	None
n_epochs	Number of epochs over which to train the model.	300	True	None
dropout	Fraction of neurons affected by Dropout, from 0 to 1.	0.1	True	None
batch_size	Number of time series (input and output) used in each training pass	32	True	None
s_end_date	The end date (format YYYY-MM-DD) to select for testing	None	True	None
s_start_date	The start date (format YYYY-MM-DD) to select for testing	None	True	None
residuals	Show the residuals for the model.	False	True	None
forecast_only	Do not plot the historical data without forecasts.	False	True	None
export_pred_raw	Export predictions to a csv file.	False	True	None

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Examples

2022 Oct 11, 06:38 D /forecast/ $ load AAPL_20220719_201127.csv

2022 Oct 11, 06:38 D /forecast/ $ nhits AAPL_20220719_201127
Epoch 153: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 25/25 [00:0000:00, 183.87it/s, loss=-1.56, train_loss=-1.60, val_loss=-.954]
Predicting NHITS for 5 days
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:0100:00, 92.46it/s]
NHITS model obtains MAPE: 7.45%



   Actual price: 147.07
┏━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Datetime   ┃ Prediction ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ 2022-07-19 │ 134.84     │
├────────────┼────────────┤
│ 2022-07-20 │ 141.80     │
├────────────┼────────────┤
│ 2022-07-21 │ 131.06     │
├────────────┼────────────┤
│ 2022-07-22 │ 102.95     │
├────────────┼────────────┤
│ 2022-07-25 │ 123.72     │
└────────────┴────────────┘

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