forecastHybrid vs forecast

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

forecastHybrid

Automated ensemble forecasting with ARIMA, ETS, STLM, TBATS, and neural networks.

forecast

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

At a glance

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

Overview

One-liner

Automated ensemble forecasting with ARIMA, ETS, STLM, TBATS, and neural networks.

One-liner

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

Maturity

Not enough data

Maturity

Not enough data

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

R users who need to quickly generate accurate forecasts using multiple models
Researchers and analysts looking for a streamlined approach to time series analysis

✓ 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 ideal for

✕ Not for

Users requiring real-time forecasting capabilities
Projects that require integration with non-R environments

✕ 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

Strengths

↑ Strengths

Automated ensemble forecasting
Cross-validation of multiple models
Support for ARIMA, ETS, STL, TBATS, and neural networks
Simplified model selection process

↑ Strengths

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

Weaknesses

↓ Weaknesses

Limited to R users, not accessible for developers in other languages
Performance issues with large datasets
Documentation lacks depth and examples for advanced use cases

↓ 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
Full forecastHybrid profile →Full forecast profile →