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Mention that timestamps need to regularly spaced

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Oleksandr Shchur 2026-01-19 12:44:07 +00:00
parent 38f3ef1bb0
commit b1d63c61c4
3 changed files with 9 additions and 5 deletions
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3
src/chronos/base.py
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@ -168,7 +168,8 @@ class BaseChronosPipeline(metaclass=PipelineRegistry):
[ADVANCED] When True (default), validates dataframes before prediction. Setting to False removes the
validation overhead, but may silently lead to wrong predictions if data is misformatted. When False, you
must ensure: (1) all dataframes are sorted by (id_column, timestamp_column); (2) future_df (if provided)
has the same item IDs as df with exactly prediction_length rows of future timestamps per item.
has the same item IDs as df with exactly prediction_length rows of future timestamps per item; (3) all
timestamps are regularly spaced (e.g., with hourly frequency).
freq
Frequency string for timestamp generation (e.g., "h", "D", "W"). Can only be used when
validate_inputs=False. When provided, skips frequency inference from the data.

3
src/chronos/chronos2/pipeline.py
View file

@ -868,7 +868,8 @@ class Chronos2Pipeline(BaseChronosPipeline):
[ADVANCED] When True (default), validates dataframes before prediction. Setting to False removes the
validation overhead, but may silently lead to wrong predictions if data is misformatted. When False, you
must ensure: (1) all dataframes are sorted by (id_column, timestamp_column); (2) future_df (if provided)
has the same item IDs as df with exactly prediction_length rows of future timestamps per item.
has the same item IDs as df with exactly prediction_length rows of future timestamps per item; (3) all
timestamps are regularly spaced (e.g., with hourly frequency).
freq
Frequency string for timestamp generation (e.g., "h", "D", "W"). Can only be used when
validate_inputs=False. When provided, skips frequency inference from the data.

8
src/chronos/df_utils.py
View file

@ -234,7 +234,8 @@ def convert_df_input_to_list_of_dicts_input(
[ADVANCED] When True (default), validates dataframes before prediction. Setting to False removes the
validation overhead, but may silently lead to wrong predictions if data is misformatted. When False, you
must ensure: (1) all dataframes are sorted by (id_column, timestamp_column); (2) future_df (if provided)
has the same item IDs as df with exactly prediction_length rows of future timestamps per item.
has the same item IDs as df with exactly prediction_length rows of future timestamps per item; (3) all
timestamps are regularly spaced (e.g., with hourly frequency).
freq
Frequency string for timestamp generation (e.g., "h", "D", "W"). Can only be used
when validate_inputs=False. When provided, skips frequency inference from the data.
@ -254,8 +255,9 @@ def convert_df_input_to_list_of_dicts_input(
"freq can only be provided when validate_inputs=False. "
"When using freq with validate_inputs=False, you must ensure: "
"(1) all dataframes are sorted by (id_column, timestamp_column); "
"future_df (if provided) has the same item IDs as df with exactly "
"prediction_length rows of future timestamps per item."
"(2) future_df (if provided) has the same item IDs as df with exactly "
"prediction_length rows of future timestamps per item; "
"(3) all timestamps are regularly spaced."
)
if validate_inputs: