•Edwin Ng, Lindsey Elkin, Yifeng Wu, Jing Pan, Ariel Jiang, Steve Yang•10 min read•intermediate•
--
•View OriginalOverview
The article introduces Orbit, an open-source package designed for Bayesian time series inference and forecasting. It highlights Orbit's flexibility, ease of use, and high performance, making it suitable for various applications, particularly in marketing data science at Uber.
What You'll Learn
1
How to implement Bayesian time series models using Orbit
2
Why Orbit is a flexible tool for time series forecasting
3
When to use LGT and DLT models for forecasting
Prerequisites & Requirements
- Understanding of Bayesian statistics and time series analysis
- Familiarity with Python and relevant libraries like Stan or Pyro(optional)
Key Questions Answered
What is the purpose of the Orbit package?
Orbit is designed for Bayesian time series modeling and forecasting, providing a consistent interface for users to conduct inferences and predictions. It aims to bridge the gap between business problems and statistical solutions, allowing for flexible model specifications.
How does Orbit compare to other time series forecasting tools?
Orbit is positioned as a flexible tool that combines the ease of model specification with a wide range of modeling options. Unlike Prophet, which offers a single model type, Orbit allows users to explore various models while maintaining an end-to-end solution.
What are the main models implemented in Orbit?
Orbit currently implements two major types of Bayesian structural time series models: the Seasonal Local/Global Trend Model (LGT) and the Damped Local Trend Model (DLT). These models are designed to handle different forecasting scenarios effectively.
What is the significance of backtesting in Orbit?
Backtesting in Orbit is crucial for evaluating the performance of time series forecasting models. It supports two schemes: expanding window and rolling window, allowing users to assess how well their models predict future data based on historical performance.
Key Statistics & Figures
Performance improvement over other models
12% to 60%
Orbit models demonstrated competitive results in benchmark studies compared to other time series forecasting models.
Technologies & Tools
Probabilistic Programming Language
Stan
Used for posterior approximation in Orbit.
Probabilistic Programming Language
Pyro
Utilized for MCMC sampling and SVI in Orbit.
Key Actionable Insights
1Utilize Orbit's flexible modeling capabilities to tailor your forecasting approach based on specific business needs.This flexibility allows data scientists to adapt models to various scenarios, improving the accuracy and relevance of forecasts in dynamic environments.
2Leverage the backtesting utilities provided by Orbit to validate your models before deployment.Backtesting helps identify potential issues and refine model parameters, ensuring that the forecasts are reliable and actionable.
3Explore the integration of Orbit with other tools like Stan and Pyro for advanced Bayesian modeling.This integration allows for sophisticated posterior approximations and enhances the overall modeling capabilities, making Orbit a powerful choice for data-driven decision-making.
Common Pitfalls
1
Assuming that one model type fits all forecasting scenarios can lead to inaccurate predictions.
Different business contexts and data characteristics require tailored modeling approaches. It’s essential to explore various models and validate them through backtesting.
Related Concepts
Bayesian Statistics
Time Series Analysis
Probabilistic Programming