forecast
Time series forecasting using ARIMA, ETS, STLM, TBATS, and neural network models.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
UnverifiedOverview
What is forecast?
The forecast package in R provides methods for time series forecasting including automatic ARIMA fitting, exponential smoothing state space model (ETS), STL decomposition, TBATS modeling, and neural networks. It is essential for data scientists and analysts who need to predict future values based on historical data.
Key differentiator
“forecast stands out for its comprehensive suite of time series forecasting methods, including advanced models like TBATS and neural networks, making it ideal for complex seasonal patterns.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
The package primarily focuses on ARIMA, ETS, STL decomposition, TBATS and neural networks, but lacks support for more sophisticated models like VAR or state-space models beyond ETS.
The package can be slow when handling very large time series datasets due to the computational intensity of its methods, particularly in automatic model selection processes.
While there are examples and a vignette, the documentation lacks detailed explanations for each function's parameters and output interpretation, making it difficult for new users to fully utilize the package.
The forecast package does not have built-in support for integrating with databases, cloud storage services, or other popular data manipulation and visualization libraries in R beyond base functionalities.
Fit analysis
Who is it 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
✕ Not a fit 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
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Next step
Get Started with forecast
Step-by-step setup guide with code examples and common gotchas.