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Usage of DeepRenewal
usage: deeprenewal [-h] [--use-cuda USE_CUDA]
[--datasource {retail_dataset}]
[--regenerate-datasource REGENERATE_DATASOURCE]
[--model-save-dir MODEL_SAVE_DIR]
[--point-forecast {median,mean}]
[--calculate-spec CALCULATE_SPEC]
[--batch_size BATCH_SIZE]
[--learning-rate LEARNING_RATE]
[--max-epochs MAX_EPOCHS]
[--number-of-batches-per-epoch NUMBER_OF_BATCHES_PER_EPOCH]
[--clip-gradient CLIP_GRADIENT]
[--weight-decay WEIGHT_DECAY]
[--context-length-multiplier CONTEXT_LENGTH_MULTIPLIER]
[--num-layers NUM_LAYERS]
[--num-cells NUM_CELLS]
[--cell-type CELL_TYPE]
[--dropout-rate DROPOUT_RATE]
[--use-feat-dynamic-real USE_FEAT_DYNAMIC_REAL]
[--use-feat-static-cat USE_FEAT_STATIC_CAT]
[--use-feat-static-real USE_FEAT_STATIC_REAL]
[--scaling SCALING]
[--num-parallel-samples NUM_PARALLEL_SAMPLES]
[--num-lags NUM_LAGS]
[--forecast-type FORECAST_TYPE]
GluonTS implementation of paper 'Intermittent Demand Forecasting with Deep
Renewal Processes'
optional arguments:
-h, --help show this help message and exit
--use-cuda USE_CUDA
--datasource {retail_dataset}
--regenerate-datasource REGENERATE_DATASOURCE
Whether to discard locally saved dataset and
regenerate from source
--model-save-dir MODEL_SAVE_DIR
Folder to save models
--point-forecast {median,mean}
How to estimate point forecast? Mean or Median
--calculate-spec CALCULATE_SPEC
Whether to calculate SPEC. It is computationally
expensive and therefore False by default
--batch_size BATCH_SIZE
--learning-rate LEARNING_RATE
--max-epochs MAX_EPOCHS
--number-of-batches-per-epoch NUMBER_OF_BATCHES_PER_EPOCH
--clip-gradient CLIP_GRADIENT
--weight-decay WEIGHT_DECAY
--context-length-multiplier CONTEXT_LENGTH_MULTIPLIER
If context multipler is 2, context available to hte
RNN is 2*prediction length
--num-layers NUM_LAYERS
--num-cells NUM_CELLS
--cell-type CELL_TYPE
--dropout-rate DROPOUT_RATE
--use-feat-dynamic-real USE_FEAT_DYNAMIC_REAL
--use-feat-static-cat USE_FEAT_STATIC_CAT
--use-feat-static-real USE_FEAT_STATIC_REAL
--scaling SCALING Whether to scale targets or not
--num-parallel-samples NUM_PARALLEL_SAMPLES
--num-lags NUM_LAGS Number of lags to be included as feature
--forecast-type FORECAST_TYPE
Defines how the forecast is decoded. For details look
at the documentation
An example of training process is as follows:
python3 deeprenewal --datasource retail_dataset --lr 0.001 --epochs 50
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