Greykite: A flexible, intuitive and fast forecasting and anomaly detection library
The Greykite library provides flexible, intuitive and fast forecasts through its flagship algorithm, Silverkite.
Silverkite algorithm works well on most time series, and is especially adept for those with changepoints in trend or seasonality, event/holiday effects, and temporal dependencies. Its forecasts are interpretable and therefore useful for trusted decision-making and insights.
The Greykite library provides a framework that makes it easy to develop a good forecast model, with exploratory data analysis, outlier/anomaly preprocessing, feature extraction and engineering, grid search, evaluation, benchmarking, and plotting. Other open source algorithms can be supported through Greykite’s interface to take advantage of this framework, as listed below.
Greykite AD (Anomaly Detection) is an extension of the Greykite Forecasting library. It provides users with an interpretable, fast, robust and easy to use interface to monitor their metrics with minimal effort.
Greykite AD improves upon the out-of-box confidence intervals generated by Silverkite, by automatically tuning the confidence intervals
and other filters (e.g. based on APE) using expected alert rate information and/ or anomaly labels, if available.
It allows the users to define robust objective function, constraints and parameter space to optimize the confidence intervals.
For example user can target a minimal recall level of 80% while maximizing precision. Additionally, the users can specify a
minimum error level to filter out anomalies that are not business relevant. The motivation to include criteria other than
statistical significance is to bake in material/ business impact into the detection.
For a demo, please see our quickstart.
- Flexible design
- Provides time series regressors to capture trend, seasonality, holidays, changepoints, and autoregression, and lets you add your own.
- Fits the forecast using a machine learning model of your choice.
- Intuitive interface
- Provides powerful plotting tools to explore seasonality, interactions, changepoints, etc.
- Provides model templates (default parameters) that work well based on data characteristics and forecast requirements (e.g. daily long-term forecast).
- Produces interpretable output, with model summary to examine individual regressors, and component plots to visually inspect the combined effect of related regressors.
- Fast training and scoring
- Facilitates interactive prototyping, grid search, and benchmarking. Grid search is useful for model selection and semi-automatic forecasting of multiple metrics.
- Extensible framework
- Exposes multiple forecast algorithms in the same interface, making it easy to try algorithms from different libraries and compare results.
- The same pipeline provides preprocessing, cross-validation, backtest, forecast, and evaluation with any algorithm.
Algorithms currently supported within Greykite’s modeling framework:
- Silverkite (Greykite’s flagship forecasting algorithm)
- Greykite Anomaly Detection (Greykite's flagship anomaly detection algorithm)
- Facebook Prophet
- Auto Arima
Greykite offers components that could be used within other forecasting libraries or even outside the forecasting context.
- ModelSummary() - R-like summaries of scikit-learn and statsmodels regression models.
- ChangepointDetector() - changepoint detection based on adaptive lasso, with visualization.
- SimpleSilverkiteForecast() - Silverkite algorithm with forecast_simple and predict methods.
- SilverkiteForecast() - low-level interface to Silverkite algorithm with forecast and predict methods.
- ReconcileAdditiveForecasts() - adjust a set of forecasts to satisfy inter-forecast additivity constraints.
- GreykiteDetector() - simple interface for optimizing anomaly detection performance based on Greykite forecasts.
You can obtain forecasts with only a few lines of code:
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
# 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
)
# 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 functionsFor a demo, please see our quickstart.
Greykite is available on Pypi and can be installed with pip:
pip install greykite
For more installation tips, see installation.
Please find our full documentation here.
Please cite Greykite in your publications if it helps your research:
@misc{reza2021greykite-github,
author = {Reza Hosseini and
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},
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}
}
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