CLI commands

Basic forecast usage:

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

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
    [RAW_OUTPUT]  by default we return only forecast without features  [default: False]

How to create config?

Example of pipeline’s config:

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

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]

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

How to create configs?

Example of pipeline’s config:

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

Example of backtest run config:

n_folds: 3
n_jobs: 3
metrics:
  - _target_: etna.metrics.MAE
  - _target_: etna.metrics.MSE
  - _target_: etna.metrics.MAPE
  - _target_: etna.metrics.SMAPE

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