prophetverse 0.10.1

A multiverse of prophet models, for forecasting and Marketing Mix Modeling.

Prophetverse

Prophetverse leverages the theory behind the Prophet model for time series forecasting and expands it into a more general framework, enabling custom priors, non-linear effects for exogenous variables and other likelihoods. Built on top of sktime and numpyro, Prophetverse aims to provide a flexible and easy-to-use library for time series forecasting with a focus on interpretability and customizability. It is particularly useful for Marketing Mix Modeling, where understanding the effect of different marketing channels on sales is crucial.

Table of Contents

• 🚀 Installation
• 📊 Forecasting with default hyperparameters
• 🌟 Features & Comparison with Meta's Prophet
• 🤝 Contributing to Prophetverse
• 📚 Documentation
• 🗺 Roadmap

🚀 Installation

To install with pip:

pip install prophetverse

Or with poetry:

poetry add prophetverse

📊 Forecasting with default hyperparameters

Prophetverse model provides an interface compatible with sktime. Here's how to use it:

from prophetverse.sktime import Prophetverse

# Create the model
model = Prophetverse()

# Fit the model
model.fit(y=y, X=X)

# Forecast in sample
y_pred = model.predict(X=X, fh=[1,2,3,4])

🌟 Features & Comparison with Meta's Prophet

Prophetverse is similar to the original Prophet model in many aspects, but it has some differences and new features. The following table summarizes the main features of Prophetverse and compares them with the original Prophet model:

Feature	Prophetverse	Original Prophet	Motivation
Logistic trend	Capacity as a random variable	Capacity as a hyperparameter, user input required	The capacity is usually unknown by the users. Having it as a variable is useful for Total Addressable Market inference
Custom trend	Customizable trend functions	Not available	Users can create custom trends and leverage their knowledge about the timeseries to enhance long-term accuracy
Likelihoods	Gaussian, Gamma and Negative Binomial	Gaussian only	Gaussian likelihood fails to provide good forecasts to positive-only and count data (sales, for example)
Custom priors	Supports custom priors for model parameters and exogenous variables	Not supported	Forcing positive coefficients, using prior knowledge to model the timeseries
Custom exogenous effects	Non-linear and customizable effects for exogenous variables, shared coefficients between time series	Not available	Users can create any kind of relationship between exogenous variables and the timeseries, which can be useful for Marketing Mix Modeling and other applications.
Changepoints	Uses changepoint interval	Uses changepoint number	The changepoint number is not stable in the sense that, when the size of timeseries increases, its impact on forecast changes. Think about setting a changepoint number when timeseries has 6 months, and forecasting in future with 2 years of data (4x time original size). Re-tuning would be required. Prophetverse is expected to be more stable
Scaling	Time series scaled internally, exogenous variables scaled by the user	Time series scaled internally	Scaling y is needed to enhance user experience with hyperparameters. On the other hand, not scaling the exogenous variables provide more control to the user and they can leverage sktime's transformers to handle that.
Seasonality	Fourier terms for seasonality passed as exogenous variables	Built-in seasonality handling	Setting up seasonality requires almost zero effort by using LinearFourierSeasonality in Prophetverse. The idea is to allow the user to create custom seasonalities easily, without hardcoding it in the code.
Multivariate model	Hierarchical model with multivariate normal likelihood and LKJ prior, bottom-up forecast	Not available	Having shared coefficients, using global information to enhance individual forecast.
Implementation	Numpyro	Stan

🤝 Contributing to Prophetverse

We welcome contributions! Check out our contributing guidelines to get started.

📚 Documentation

Detailed documentation is available here