From 75a2dfe228e39d55283f477c01399b9e53d89661 Mon Sep 17 00:00:00 2001 From: Abdul Fatir Date: Mon, 8 Dec 2025 14:12:26 +0000 Subject: [PATCH] Update docstring --- src/chronos/base.py | 3 ++- src/chronos/chronos2/pipeline.py | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/src/chronos/base.py b/src/chronos/base.py index 2f077e5..11fcd5c 100644 --- a/src/chronos/base.py +++ b/src/chronos/base.py @@ -164,7 +164,8 @@ class BaseChronosPipeline(metaclass=PipelineRegistry): quantile_levels Quantile levels to compute validate_inputs - When True, the dataframe(s) will be validated before prediction + When True, the dataframe(s) will be validated before prediction, ensuring that timestamps have a + regular frequency, and item IDs match between past and future data. Setting to False disables these checks. **predict_kwargs Additional arguments passed to predict_quantiles diff --git a/src/chronos/chronos2/pipeline.py b/src/chronos/chronos2/pipeline.py index 93e7d3b..3d22a8e 100644 --- a/src/chronos/chronos2/pipeline.py +++ b/src/chronos/chronos2/pipeline.py @@ -851,7 +851,8 @@ class Chronos2Pipeline(BaseChronosPipeline): For optimal results, consider using a batch size around 100 (as used in the Chronos-2 technical report). - Cross-learning is most helpful when individual time series have limited historical context, as the model can leverage patterns from related series in the batch. validate_inputs - When True, the dataframe(s) will be validated before prediction + When True, the dataframe(s) will be validated before prediction, ensuring that timestamps have a + regular frequency, and item IDs match between past and future data. Setting to False disables these checks. **predict_kwargs Additional arguments passed to predict_quantiles