CLI commands¶

Basic `forecast` usage:¶

Usage: etna forecast [OPTIONS] CONFIG_PATH TARGET_PATH FREQ OUTPUT_PATH [EXOG_PATH]
                     [FORECAST_CONFIG_PATH] [RAW_OUTPUT] [KNOWN_FUTURE]

Command to make forecast with etna without coding.

Arguments:
    CONFIG_PATH             path to yaml config with desired pipeline  [required]
    TARGET_PATH             path to csv with data to forecast  [required]
    FREQ                    frequency of timestamp in files in pandas format  [required]
    OUTPUT_PATH             where to save forecast  [required]
    [EXOG_PATH]             path to csv with exog data
    [FORECAST_CONFIG_PATH]  path to yaml config with forecast params
    [RAW_OUTPUT]            by default we return only forecast without features [default: False]
    [KNOWN_FUTURE]          list of all known_future columns (regressor columns). If not specified then all exog_columns considered known_future [default: None]

How to create config?

Example of pipeline’s config:

_target_: etna.pipeline.Pipeline
horizon: 4
model:
  _target_: etna.models.CatBoostMultiSegmentModel
transforms:
  - _target_: etna.transforms.LinearTrendTransform
    in_column: target
  - _target_: etna.transforms.SegmentEncoderTransform

Example of forecast params config:

prediction_interval: true
quantiles: [0.025, 0.975]
n_folds: 3

How to prepare data?

Example of dataset with data to forecast:

timestamp	segment	target
2020-01-01	segment_1	1
2020-01-02	segment_1	2
2020-01-03	segment_1	3
2020-01-04	segment_1	4
…
2020-01-10	segment_2	10
2020-01-11	segment_2	20

Example of exog dataset:

timestamp	segment	regressor_1	regressor_2
2020-01-01	segment_1	11	12
2020-01-02	segment_1	22	13
2020-01-03	segment_1	31	14
2020-01-04	segment_1	42	15
…
2020-02-10	segment_2	101	61
2020-02-11	segment_2	205	54

Basic `backtest` usage:¶

Usage: etna backtest [OPTIONS] CONFIG_PATH BACKTEST_CONFIG_PATH TARGET_PATH FREQ OUTPUT_PATH [EXOG_PATH] [KNOWN_FUTURE]

Command to run backtest with etna without coding.

Arguments:
    CONFIG_PATH             path to yaml config with desired pipeline  [required]
    BACKTEST_CONFIG_PATH    path to yaml with backtest run config [required]
    TARGET_PATH             path to csv with data to forecast  [required]
    FREQ                    frequency of timestamp in files in pandas format  [required]
    OUTPUT_PATH             where to save forecast  [required]
    [EXOG_PATH]             path to csv with exog data
    [KNOWN_FUTURE]          list of all known_future columns (regressor columns). If not specified then all exog_columns considered known_future [default: None]

How to create configs?