What is Orbit in Python?

Orbit in Python refers to a powerful and intuitive open-source Python package designed specifically for Bayesian time series forecasting and inference. It simplifies complex time series tasks by providing a familiar initialize-fit-predict interface, making advanced probabilistic modeling accessible to data scientists and developers.

Introduction to Orbit

Orbit is a sophisticated Python package for Bayesian time series forecasting and inference. It stands out by offering a user-friendly API that mirrors common machine learning workflows, while internally leveraging the power of probabilistic programming languages (PPLs) to handle the underlying statistical computations. This approach allows users to build robust forecasting models that inherently quantify uncertainty, a critical aspect often overlooked in traditional time series methods.

Key Features and Advantages

Orbit combines ease of use with powerful statistical capabilities, offering several benefits:

• Bayesian Approach: It provides probabilistic forecasts, including uncertainty intervals (e.g., credible intervals), which are crucial for informed decision-making.
• Intuitive Interface: Adopts the standard initialize-fit-predict pattern, making it familiar to anyone who has used scikit-learn or similar libraries.
• Scalability: Designed to handle various scales of time series data efficiently.
• Flexibility: Supports different model components, allowing users to customize models for specific time series characteristics like trends, seasonality, and holidays.
• Underlying PPLs: Utilizes powerful probabilistic programming languages like PyMC and Stan, abstracting away their complexity for the user.
• Open Source: Being an open-source project, it benefits from community contributions and transparency.

How Orbit Works: The initialize-fit-predict Interface

The core of Orbit's design philosophy is its streamlined initialize-fit-predict workflow. This pattern simplifies the process of building and deploying time series models:

1. Initialize (Model Definition): You define the model structure, specifying components like trend, seasonality, and regressors.
2. Fit (Model Training): The model learns patterns from your historical data. During this phase, Orbit uses Bayesian inference to estimate the posterior distributions of the model parameters.
3. Predict (Forecasting): Once fitted, the model can generate future forecasts, complete with uncertainty estimates.

This structured approach makes it straightforward to experiment with different model configurations and evaluate their performance.

Core Capabilities of Orbit

Orbit is well-equipped for a range of time series analytics tasks:

• Forecasting: Generating future predictions for various time series, such as sales, demand, or website traffic.
• Uncertainty Quantification: Providing credible intervals around forecasts, offering a measure of confidence in the predictions.
• Anomaly Detection: Identifying unusual data points or patterns that deviate significantly from the expected behavior.
• Causal Inference (with Regressors): Incorporating external factors (regressors) to understand their impact on the time series and improve forecast accuracy.
• Model Interpretability: Allowing insights into how different components (trend, seasonality) contribute to the overall forecast.

Why Choose Orbit?

Choosing Orbit for your time series needs brings several distinct advantages, especially when compared to purely frequentist or classical methods:

Feature/Aspect	Orbit (Bayesian Approach)	Traditional Methods (e.g., ARIMA, ETS)
Uncertainty	Provides full posterior distributions and credible intervals.	Typically provides point estimates with frequentist confidence intervals.
Flexibility	Highly customizable models; easy to incorporate domain knowledge.	More rigid model structures; harder to integrate external information.
Small Data	Performs well with limited data by leveraging priors.	May struggle with sparse data; requires sufficient observations.
Interpretability	Clear understanding of component contributions (trend, seasonality).	Can be harder to interpret individual component effects directly.
Ease of Use (API)	Familiar initialize-fit-predict interface.	Varies; some can be complex for specific configurations.

Getting Started with Orbit (Installation & Basic Usage)

Getting Orbit up and running is straightforward.

Installation

You can install Orbit using pip:

pip install orbit-ml

It's recommended to install it within a virtual environment. For specific backend dependencies (like pystan), you might need to install those separately if you encounter issues, though orbit-ml often handles common ones.

Basic Forecasting Example

Here's a simplified example demonstrating how to use Orbit for basic forecasting:

import pandas as pd
import numpy as np
from orbit.models.lgt import LGT
# from orbit.diagnostics.plot import plot_predicted_data # Uncomment to visualize

# 1. Prepare Data (Example: Daily sales data)
# 'ds' for datestamp, 'y' for observed value
data = {
    'ds': pd.to_datetime(pd.date_range(start='2022-01-01', periods=100, freq='D')),
    'y': [i + (i % 7) * 5 + 20 + np.random.normal(0, 3) for i in range(100)]
}
df = pd.DataFrame(data)

# 2. Initialize the Model (e.g., Local Global Trend - LGT)
# Orbit offers various models like LGT, DLT (Dynamic Linear Trend), 
# KTR (Kernel-based Trend) and more specialized ones for different backends.
# Here, we use LGT as an example.
forecaster = LGT(
    response_col='y',
    date_col='ds',
    seed=888, # for reproducibility
)

# 3. Fit the Model to Historical Data
forecaster.fit(df)

# 4. Predict Future Values
# Define the number of future periods to forecast
future_df = forecaster.make_future_dataframe(periods=30)
predicted_df = forecaster.predict(df=future_df)

# Display predictions (showing date, point prediction, and 5%/95% credible intervals)
print(predicted_df[['ds', 'prediction', 'prediction_5', 'prediction_95']].head())

# Optional: Plotting the results
# You would typically install matplotlib and seaborn for plotting
# plot_predicted_data(training_actual_df=df, predicted_df=predicted_df, 
#                     date_col='ds', actual_col='y', 
#                     prediction_col='prediction', lower_col='prediction_5', 
#                     upper_col='prediction_95')

This example demonstrates the simplicity of defining a model, fitting it to your data, and generating future predictions with associated uncertainty bands.

Under the Hood: Probabilistic Programming

Orbit cleverly abstracts the complexity of probabilistic programming languages (PPLs). When you call forecaster.fit(df), Orbit translates your model definition into a probabilistic model specification that can be solved by a PPL backend. Common backends include:

• PyMC: A popular Python library for probabilistic programming, built on Aesara (or Theano).
• Stan: A state-of-the-art platform for statistical modeling and high-performance statistical computation, accessed via PyStan or CmdStanPy.

By using these powerful tools, Orbit enables sophisticated Bayesian inference, allowing it to estimate full posterior distributions for model parameters, which is the foundation for robust uncertainty quantification in its forecasts.