parent_command:forecast
usage: brnn [--n-rnn-layers N_RNN_LAYERS] [--past-covariates PAST_COVARIATES] [--all-past-covariates] [--naive] [-d {AAPL}] [-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] [--model-type MODEL_TYPE] [--dropout DROPOUT] [--batch-size BATCH_SIZE] [--end S_END_DATE]
            [--start S_START_DATE] [--learning-rate LEARNING_RATE] [--residuals] [--forecast-only] [--export-pred-raw] [--metric {rmse,mse,mape,smape}]

Perform BRNN forecast (Vanilla RNN, LSTM, GRU): https://unit8co.github.io/darts/generated_api/darts.models.forecasting.block_rnn_model.html

optional arguments:
  --n-rnn-layers N_RNN_LAYERS
                        Number of layers in the RNN module. (default: 1)
  --past-covariates PAST_COVARIATES
                        Past covariates(columns/features) in same dataset. Comma separated. (default: None)
  --all-past-covariates
                        Adds all rows as past covariates except for date and the target column. (default: False)
  --naive               Show the naive baseline for a model. (default: False)
  -d {AAPL}, --dataset {AAPL}
                        The name of the dataset you want to select (default: None)
  -c TARGET_COLUMN, --target-column TARGET_COLUMN
                        The name of the specific column you want to use (default: close)
  -n N_DAYS, --n-days N_DAYS
                        prediction days. (default: 5)
  -t TRAIN_SPLIT, --train-split TRAIN_SPLIT
                        Start point for rolling training and forecast window. 0.0-1.0 (default: 0.85)
  -i INPUT_CHUNK_LENGTH, --input-chunk-length INPUT_CHUNK_LENGTH
                        Number of past time steps for forecasting module at prediction time. (default: 14)
  -o OUTPUT_CHUNK_LENGTH, --output-chunk-length OUTPUT_CHUNK_LENGTH
                        The length of the forecast of the model. (default: 5)
  --force-reset FORCE_RESET
                        If set to True, any previously-existing model with the same name will be reset (all checkpoints will be discarded). (default: True)
  --save-checkpoints SAVE_CHECKPOINTS
                        Whether to automatically save the untrained model and checkpoints. (default: True)
  --model-save-name MODEL_SAVE_NAME
                        Name of the model to save. (default: brnn_model)
  --n-epochs N_EPOCHS   Number of epochs over which to train the model. (default: 300)
  --model-type MODEL_TYPE
                        Enter a string specifying the RNN module type ("RNN", "LSTM" or "GRU") (default: LSTM)
  --dropout DROPOUT     Fraction of neurons affected by Dropout, from 0 to 1. (default: 0)
  --batch-size BATCH_SIZE
                        Number of time series (input and output) used in each training pass (default: 32)
  --end S_END_DATE      The end date (format YYYY-MM-DD) to select for testing (default: None)
  --start S_START_DATE  The start date (format YYYY-MM-DD) to select for testing (default: None)
  --learning-rate LEARNING_RATE
                        Learning rate during training. (default: 0.001)
  --residuals           Show the residuals for the model. (default: False)
  --forecast-only       Do not plot the historical data without forecasts. (default: False)
  --export-pred-raw     Export predictions to a csv file. (default: False)
  --metric {rmse,mse,mape,smape}
                        Calculate precision based on a specific metric (rmse, mse, mape) (default: mape)


Examples:
- Perform a BRNN forecast using the default settings on the AAPL dataset: forecast/brnn -d AAPL
- Perform a BRNN forecast with LSTM and 10 prediction days on the AAPL dataset: forecast/brnn -d AAPL --model-type LSTM -n 10
- Perform a BRNN forecast with 3 RNN layers and a dropout of 0.2 on the AAPL dataset: forecast/brnn -d AAPL --n-rnn-layers 3 --dropout 0.2
- Perform a BRNN forecast using the GRU model type and a batch size of 64 on the AAPL dataset: forecast/brnn -d AAPL --model-type GRU --batch-size 64
- Perform a BRNN forecast with a custom train split of 0.9 and 500 epochs on the AAPL dataset: forecast/brnn -d AAPL -t 0.9 --n-epochs 500
- Perform a BRNN forecast with specific start and end dates on the AAPL dataset: forecast/brnn -d AAPL --start 2020-01-01 --end 2021-12-31
- Perform a BRNN forecast using all past covariates except for date and target column on the AAPL dataset: forecast/brnn -d AAPL --all-past-covariates
- Perform a BRNN forecast and calculate the model's precision using the RMSE metric on the AAPL dataset: forecast/brnn -d AAPL --metric rmse
- Perform a BRNN forecast and export the raw predictions to a CSV file on the AAPL dataset: forecast/brnn -d AAPL --export-pred-raw
- Perform a BRNN forecast with a custom input chunk length of 20 and output chunk length of 10 on the AAPL dataset: forecast/brnn -d AAPL -i 20 -o 10