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Updated README citations (#92)
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al-bert authored Aug 31, 2022
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63 changes: 37 additions & 26 deletions README.rst
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Expand Up @@ -70,29 +70,31 @@ You can obtain forecasts with only a few lines of code:

.. code-block:: python
from greykite.common.data_loader import DataLoader
from greykite.framework.templates.autogen.forecast_config import ForecastConfig
from greykite.framework.templates.autogen.forecast_config import MetadataParam
from greykite.framework.templates.forecaster import Forecaster
from greykite.framework.templates.model_templates import ModelTemplateEnum
# df = ... # your input timeseries!
metadata = MetadataParam(
time_col="ts", # time column in `df`
value_col="y" # value column in `df`
)
forecaster = Forecaster() # creates forecasts and stores the result
forecaster.run_forecast_config(
df=df,
config=ForecastConfig(
model_template=ModelTemplateEnum.AUTO.name, # automatically selects model parameters
forecast_horizon=365, # forecasts 365 steps ahead
coverage=0.95, # 95% prediction intervals
metadata_param=metadata
)
# Defines inputs
df = DataLoader().load_bikesharing().tail(24*90) # Input time series (pandas.DataFrame)
config = ForecastConfig(
metadata_param=MetadataParam(time_col="ts", value_col="count"), # Column names in `df`
model_template=ModelTemplateEnum.AUTO.name, # AUTO model configuration
forecast_horizon=24, # Forecasts 24 steps ahead
coverage=0.95, # 95% prediction intervals
)
# Access the result
forecaster.forecast_result
# ...
# Creates forecasts
forecaster = Forecaster()
result = forecaster.run_forecast_config(df=df, config=config)
# Accesses results
result.forecast # Forecast with metrics, diagnostics
result.backtest # Backtest with metrics, diagnostics
result.grid_search # Time series CV result
result.model # Trained model
result.timeseries # Processed time series with plotting functions
For a demo, please see our `quickstart <https://linkedin.github.io/greykite/get_started>`_.

Expand All @@ -116,7 +118,7 @@ Learn More
----------

* `Website <https://linkedin.github.io/greykite>`_
* `Paper <https://arxiv.org/abs/2105.01098>`_
* `Paper <https://doi.org/10.1145/3534678.3539165>`_ (KDD '22 Best Paper Runner-up, Applied Data Science Track)
* `Blog post <https://engineering.linkedin.com/blog/2021/greykite--a-flexible--intuitive--and-fast-forecasting-library>`_

Citation
Expand All @@ -131,6 +133,7 @@ Please cite Greykite in your publications if it helps your research:
Albert Chen and
Kaixu Yang and
Sayan Patra and
Yi Su and
Rachit Arora},
title = {Greykite: a flexible, intuitive and fast forecasting library},
url = {https://github.com/linkedin/greykite},
Expand All @@ -139,16 +142,24 @@ Please cite Greykite in your publications if it helps your research:
.. code-block::
@misc{reza2021greykite-paper,
author = {Reza Hosseini and
Kaixu Yang and
Albert Chen and
Sayan Patra},
title = {A flexible forecasting model for production systems},
url = {https://arxiv.org/abs/2105.01098},
year = {2021}
@inproceedings{reza2022greykite-kdd,
author = {Hosseini, Reza and Chen, Albert and Yang, Kaixu and Patra, Sayan and Su, Yi and Al Orjany, Saad Eddin and Tang, Sishi and Ahammad, Parvez},
title = {Greykite: Deploying Flexible Forecasting at Scale at LinkedIn},
year = {2022},
isbn = {9781450393850},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3534678.3539165},
doi = {10.1145/3534678.3539165},
booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages = {3007–3017},
numpages = {11},
keywords = {forecasting, scalability, interpretable machine learning, time series},
location = {Washington DC, USA},
series = {KDD '22}
}
License
-------

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64 changes: 37 additions & 27 deletions README_PYPI.rst
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Expand Up @@ -7,7 +7,6 @@
:alt: Greykite
:align: center


Why Greykite?
-------------

Expand Down Expand Up @@ -72,29 +71,31 @@ You can obtain forecasts with only a few lines of code:

.. code-block:: python
from greykite.common.data_loader import DataLoader
from greykite.framework.templates.autogen.forecast_config import ForecastConfig
from greykite.framework.templates.autogen.forecast_config import MetadataParam
from greykite.framework.templates.forecaster import Forecaster
from greykite.framework.templates.model_templates import ModelTemplateEnum
# df = ... # your input timeseries!
metadata = MetadataParam(
time_col="ts", # time column in `df`
value_col="y" # value column in `df`
)
forecaster = Forecaster() # creates forecasts and stores the result
forecaster.run_forecast_config(
df=df,
config=ForecastConfig(
model_template=ModelTemplateEnum.AUTO.name, # automatically selects model parameters
forecast_horizon=365, # forecasts 365 steps ahead
coverage=0.95, # 95% prediction intervals
metadata_param=metadata
)
# Defines inputs
df = DataLoader().load_bikesharing().tail(24*90) # Input time series (pandas.DataFrame)
config = ForecastConfig(
metadata_param=MetadataParam(time_col="ts", value_col="count"), # Column names in `df`
model_template=ModelTemplateEnum.AUTO.name, # AUTO model configuration
forecast_horizon=24, # Forecasts 24 steps ahead
coverage=0.95, # 95% prediction intervals
)
# Access the result
forecaster.forecast_result
# ...
# Creates forecasts
forecaster = Forecaster()
result = forecaster.run_forecast_config(df=df, config=config)
# Accesses results
result.forecast # Forecast with metrics, diagnostics
result.backtest # Backtest with metrics, diagnostics
result.grid_search # Time series CV result
result.model # Trained model
result.timeseries # Processed time series with plotting functions
For a demo, please see our `quickstart <https://linkedin.github.io/greykite/get_started>`_.

Expand All @@ -118,7 +119,7 @@ Learn More
----------

* `Website <https://linkedin.github.io/greykite>`_
* `Paper <https://arxiv.org/abs/2105.01098>`_
* `Paper <https://doi.org/10.1145/3534678.3539165>`_ (KDD '22 Best Paper Runner-up, Applied Data Science Track)
* `Blog post <https://engineering.linkedin.com/blog/2021/greykite--a-flexible--intuitive--and-fast-forecasting-library>`_

Citation
Expand All @@ -133,6 +134,7 @@ Please cite Greykite in your publications if it helps your research:
Albert Chen and
Kaixu Yang and
Sayan Patra and
Yi Su and
Rachit Arora},
title = {Greykite: a flexible, intuitive and fast forecasting library},
url = {https://github.com/linkedin/greykite},
Expand All @@ -141,16 +143,24 @@ Please cite Greykite in your publications if it helps your research:
.. code-block::
@misc{reza2021greykite-paper,
author = {Reza Hosseini and
Kaixu Yang and
Albert Chen and
Sayan Patra},
title = {A flexible forecasting model for production systems},
url = {https://arxiv.org/abs/2105.01098},
year = {2021}
@inproceedings{reza2022greykite-kdd,
author = {Hosseini, Reza and Chen, Albert and Yang, Kaixu and Patra, Sayan and Su, Yi and Al Orjany, Saad Eddin and Tang, Sishi and Ahammad, Parvez},
title = {Greykite: Deploying Flexible Forecasting at Scale at LinkedIn},
year = {2022},
isbn = {9781450393850},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3534678.3539165},
doi = {10.1145/3534678.3539165},
booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages = {3007–3017},
numpages = {11},
keywords = {forecasting, scalability, interpretable machine learning, time series},
location = {Washington DC, USA},
series = {KDD '22}
}
License
-------

Expand Down

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