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rnn

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

Usageโ€‹

rnn [--hidden-dim HIDDEN_DIM] [--training_length TRAINING_LENGTH] [--naive] [-d {AAPL}] [-c TARGET_COLUMN] [-n N_DAYS] [-t TRAIN_SPLIT] [-i INPUT_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}]

Parametersโ€‹

NameParameterDescriptionDefaultOptionalChoices
hidden_dim--hidden-dimSize for feature maps for each hidden RNN layer (h_n)20TrueNone
training_length--training_lengthThe length of both input (target and covariates) and output (target) time series used during training. Generally speaking, training_length should have a higher value than input_chunk_length because otherwise during training the RNN is never run for as many iterations as it will during training.20TrueNone
naive--naiveShow the naive baseline for a model.FalseTrueNone
target_dataset-d --datasetThe name of the dataset you want to selectNoneTrueAAPL
target_column-c --target-columnThe name of the specific column you want to usecloseTrueNone
n_days-n --n-daysprediction days.5TrueNone
train_split-t --train-splitStart point for rolling training and forecast window. 0.0-1.00.85TrueNone
input_chunk_length-i --input-chunk-lengthNumber of past time steps for forecasting module at prediction time.14TrueNone
force_reset--force-resetIf set to True, any previously-existing model with the same name will be reset (all checkpoints will be discarded).TrueTrueNone
save_checkpoints--save-checkpointsWhether to automatically save the untrained model and checkpoints.TrueTrueNone
model_save_name--model-save-nameName of the model to save.rnn_modelTrueNone
n_epochs--n-epochsNumber of epochs over which to train the model.300TrueNone
model_type--model-typeEnter a string specifying the RNN module type ("RNN", "LSTM" or "GRU")LSTMTrueNone
dropout--dropoutFraction of neurons affected by Dropout, from 0 to 1.0TrueNone
batch_size--batch-sizeNumber of time series (input and output) used in each training pass32TrueNone
s_end_date--endThe end date (format YYYY-MM-DD) to select for testingNoneTrueNone
s_start_date--startThe start date (format YYYY-MM-DD) to select for testingNoneTrueNone
learning_rate--learning-rateLearning rate during training.0.001TrueNone
residuals--residualsShow the residuals for the model.FalseTrueNone
forecast_only--forecast-onlyDo not plot the historical data without forecasts.FalseTrueNone
export_pred_raw--export-pred-rawExport predictions to a csv file.FalseTrueNone
metric--metricCalculate precision based on a specific metric (rmse, mse, mape)mapeTruermse, mse, mape, smape

Examplesโ€‹

2022 Jul 23, 10:36 (๐Ÿฆ‹) /forecast/ $ load GME_20220719_123734.csv -a GME

2022 Jul 23, 11:03 (๐Ÿฆ‹) /forecast/ $ rnn GME
100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 115/115 [00:0700:00, 15.10it/s]
RNN model obtains MAPE: 14.67%



Actual price: $ 146.64
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“
โ”ƒ Datetime โ”ƒ Prediction โ”ƒ
โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ
โ”‚ 2022-07-19 00:00:00 โ”‚ $ 146.89 โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚ 2022-07-20 00:00:00 โ”‚ $ 148.58 โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚ 2022-07-21 00:00:00 โ”‚ $ 150.00 โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚ 2022-07-22 00:00:00 โ”‚ $ 151.23 โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚ 2022-07-25 00:00:00 โ”‚ $ 152.29 โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

rnn