forecast vs Prophet

Side-by-side — pricing, strengths, tradeoffs, and fit.

forecast

Time series forecasting using ARIMA, ETS, STLM, TBATS, and neural network models.

Prophet

Fast and automated time series forecasting framework by Facebook.

At a glance

Open SourceFree Tier
· Not enough data·From $0
Open SourceFree Tier
● Established·From $0

Overview

One-liner

Time series forecasting using ARIMA, ETS, STLM, TBATS, and neural network models.

One-liner

Fast and automated time series forecasting framework by Facebook.

Maturity

Not enough data

Maturity

Established

Open source

✓ Yes

Open source

✓ Yes

Pricing

Free tier

✓ Available

Open source — free to use

Free tier

✓ Available

Open source — free to use

Starts at

$0

Starts at

$0

Model

Flat rate

Model

Flat rate

Enterprise

✕ None

Enterprise

✕ None

Best for

✓ Best for

Researchers needing to forecast time series with complex seasonality patterns using TBATS models
Developers who require automatic ARIMA model fitting for quick and accurate predictions
Teams working on financial forecasting projects that benefit from STL decomposition

✓ Best for

Teams needing robust time series analysis with minimal setup and maintenance
Projects requiring handling of missing data and outliers in time series

Not ideal for

✕ Not for

Projects requiring real-time data processing as it is primarily a batch-processing tool
Applications needing direct integration with cloud services, as it operates locally

✕ Not for

Real-time or streaming data forecasting applications
Applications requiring complex, non-linear trend modeling beyond Prophet's capabilities

Strengths

↑ Strengths

Automatic ARIMA model fitting
Exponential smoothing state space models (ETS)
STL decomposition for seasonal and trend analysis
TBATS modeling for complex seasonality patterns

↑ Strengths

Handles missing data and outliers effectively
Supports trend changes and seasonality
Automated parameter tuning for ease of use

Weaknesses

↓ Weaknesses

Limited support for advanced time series models
Performance issues with large datasets
Documentation is not comprehensive or beginner-friendly
Limited integration with other R packages or external systems

↓ Weaknesses

Limited flexibility in modeling complex seasonality patterns
Automated parameter tuning can lead to suboptimal models for specialized use cases
Performance degradation with very large datasets
Lack of advanced anomaly detection features
Full forecast profile →Full Prophet profile →