Pros of Prophet

• User-friendly interface with automatic handling of seasonality and holidays
• Robust handling of missing data and outliers
• Extensive documentation and community support

Cons of Prophet

• Can be slower for large datasets or many time series
• Less flexibility for custom models or advanced statistical techniques
• May overfit on datasets with limited historical data

Code Comparison

Prophet:

from fbprophet import Prophet
model = Prophet()
model.fit(df)
future = model.make_future_dataframe(periods=365)
forecast = model.predict(future)

StatsForecasts:

from statsforecast import StatsForecast
from statsforecast.models import AutoARIMA
fcst = StatsForecast(df, models=[AutoARIMA()], freq='D')
forecast = fcst.forecast(h=365)

Key Differences

• Prophet focuses on an additive model with intuitive parameters, while StatsForecasts offers a variety of statistical models
• StatsForecasts is generally faster, especially for multiple time series
• Prophet provides built-in plotting and diagnostics, whereas StatsForecasts relies more on external visualization tools
• StatsForecasts offers more advanced statistical models and the ability to easily combine multiple forecasting methods

Both libraries have their strengths, with Prophet excelling in ease of use and interpretability, while StatsForecasts offers more flexibility and performance for advanced users and large-scale forecasting tasks.

Pros of sktime

• Broader scope, covering various time series tasks beyond forecasting
• Extensive ecosystem with many algorithms and transformers
• Strong integration with scikit-learn and pandas

Cons of sktime

• Steeper learning curve due to its comprehensive nature
• Potentially slower performance for some forecasting tasks
• Less focus on probabilistic forecasting compared to StatsForecasts

Code Comparison

sktime example:

from sktime.forecasting.arima import ARIMA
from sktime.datasets import load_airline

y = load_airline()
forecaster = ARIMA(order=(1, 1, 1), seasonal_order=(1, 1, 1, 12))
forecaster.fit(y)
y_pred = forecaster.predict(fh=[1, 2, 3])

StatsForecasts example:

from statsforecast import StatsForecast
from statsforecast.models import ARIMA

sf = StatsForecast(models=[ARIMA(order=(1, 1, 1), seasonal_order=(1, 1, 1, 12))])
sf.fit(df)
forecasts = sf.predict(h=3)

Both libraries offer ARIMA forecasting, but sktime provides a more scikit-learn-like API, while StatsForecasts focuses on simplicity and performance for forecasting tasks.

Pros of pmdarima

• More mature project with a longer history and larger user base
• Extensive documentation and examples for various use cases
• Supports a wider range of ARIMA-based models, including SARIMAX

Cons of pmdarima

• Slower performance, especially for large datasets or multiple time series
• Less focus on modern forecasting techniques beyond ARIMA-based models
• More complex API, requiring more code for basic forecasting tasks

Code Comparison

pmdarima:

from pmdarima import auto_arima

model = auto_arima(y, seasonal=True, m=12)
forecast = model.predict(n_periods=12)

statsforecast:

from statsforecast import StatsForecast
from statsforecast.models import AutoARIMA

fcst = StatsForecast(df, models=[AutoARIMA()], freq='M')
forecast = fcst.forecast(h=12)

Both libraries offer AutoARIMA functionality, but statsforecast provides a more streamlined API for working with multiple time series and integrates well with pandas DataFrames. pmdarima offers more granular control over model parameters, which can be beneficial for advanced users but may require more code for basic forecasting tasks.

Pros of PyFlux

• Offers a wider range of time series models, including ARIMA, GARCH, and state space models
• Provides Bayesian inference capabilities for parameter estimation
• Includes built-in plotting functions for model diagnostics and forecasts

Cons of PyFlux

• Less actively maintained, with the last update in 2018
• Slower performance for large datasets compared to StatsForecast
• Limited documentation and community support

Code Comparison

PyFlux:

from pyflux.arima import ARIMA

model = ARIMA(data=df, ar=1, ma=1, target='y')
model.fit()
forecast = model.predict(h=5)

StatsForecast:

from statsforecast import StatsForecast
from statsforecast.models import ARIMA

sf = StatsForecast(models=[ARIMA(order=(1,0,1))])
sf.fit(df)
forecast = sf.predict(h=5)

Both libraries offer similar functionality for time series forecasting, but StatsForecast provides a more modern and efficient implementation with better performance for large-scale forecasting tasks. PyFlux offers a broader range of models and Bayesian inference capabilities, but lacks recent updates and community support.

Pros of Orbit

• Supports Bayesian modeling, allowing for uncertainty quantification
• Offers a wider range of models, including custom model creation
• Provides built-in visualization tools for model diagnostics

Cons of Orbit

• Steeper learning curve due to more complex API
• Slower performance compared to StatsForecasts's optimized implementations
• Less focus on traditional statistical models

Code Comparison

StatsForecasts:

from statsforecast import StatsForecast
from statsforecast.models import AutoARIMA

sf = StatsForecast(
    models=[AutoARIMA()],
    freq='D',
    n_jobs=-1
)
forecasts = sf.forecast(df=data, h=30)

Orbit:

from orbit.models import DLT

dlt = DLT(
    response_col='y',
    date_col='ds',
    seasonality=[7, 30.5],
    num_forecast_steps=30
)
dlt.fit(df=data)
predictions = dlt.predict(df=data)

Both libraries offer time series forecasting capabilities, but they cater to different use cases. StatsForecasts focuses on traditional statistical models with high performance, while Orbit provides a more flexible framework for Bayesian modeling and custom model creation. The choice between them depends on the specific requirements of the forecasting task and the user's familiarity with different modeling approaches.

Pros of Greykite

• More comprehensive feature set for advanced forecasting scenarios
• Stronger focus on interpretability and explainability of models
• Better suited for complex, multi-variate time series forecasting

Cons of Greykite

• Steeper learning curve due to more complex API and configuration options
• Slower execution times for large datasets compared to StatsForecast
• Less emphasis on traditional statistical methods

Code Comparison

StatsForecast:

from statsforecast import StatsForecast
from statsforecast.models import AutoARIMA

sf = StatsForecast(df, models=[AutoARIMA()], freq='D')
forecasts = sf.forecast(h=30)

Greykite:

from greykite.framework.templates.autogen.forecast_config import ForecastConfig
from greykite.framework.templates.forecaster import Forecaster

forecaster = Forecaster()
result = forecaster.run_forecast_config(
    df,
    config=ForecastConfig(
        model_template="AUTO",
        forecast_horizon=30
    )
)

Both libraries offer high-level APIs for forecasting, but Greykite's approach is more configurable and verbose. StatsForecast provides a simpler interface for quick forecasting tasks, while Greykite offers more control over the forecasting process and model selection.