Pros of statsmodels

• More comprehensive and widely used in the statistical community
• Extensive documentation and active community support
• Broader range of statistical models and methods available

Cons of statsmodels

• Steeper learning curve for beginners
• Can be slower for certain operations compared to PyFlux
• Less focus on time series analysis compared to PyFlux's specialization

Code Comparison

statsmodels:

import statsmodels.api as sm
model = sm.OLS(y, X).fit()
predictions = model.predict(X_new)

PyFlux:

import pyflux as pf
model = pf.ARIMA(data=data, ar=1, ma=1, target='y')
model.fit()
predictions = model.predict(h=5)

Summary

statsmodels offers a more comprehensive suite of statistical tools with extensive documentation and community support. However, it may have a steeper learning curve and be slower for certain operations. PyFlux, on the other hand, specializes in time series analysis and may be more user-friendly for beginners in this specific domain. The code comparison shows that both libraries have different approaches to model creation and prediction, with PyFlux focusing on time series models like ARIMA.

Pros of Prophet

• More active development and larger community support
• Handles seasonality and holidays automatically
• User-friendly interface with minimal statistical knowledge required

Cons of Prophet

• Less flexible for custom model specifications
• Can be slower for large datasets
• Limited to additive models, may not handle multiplicative trends well

Code Comparison

Prophet:

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

PyFlux:

from pyflux import ARIMA
model = ARIMA(data=df, ar=1, ma=1, integ=1)
model.fit("MLE")
forecast = model.predict(h=365)

Prophet focuses on ease of use with automatic parameter selection, while PyFlux offers more control over model specification. Prophet is better suited for quick forecasting with minimal setup, whereas PyFlux provides more flexibility for advanced users who want to fine-tune their models.

Pros of pmdarima

• More active development and maintenance
• Better documentation and examples
• Wider range of ARIMA-based models, including auto-ARIMA

Cons of pmdarima

• Focused primarily on ARIMA models, less versatile for other time series methods
• Steeper learning curve for beginners

Code Comparison

pmdarima:

from pmdarima import auto_arima

model = auto_arima(y, seasonal=True, m=12)
forecast = model.predict(n_periods=5)

pyflux:

from pyflux import ARIMA

model = ARIMA(data=data, ar=1, ma=1, integ=1)
model.fit()
forecast = model.predict(h=5)

Both libraries offer ARIMA modeling capabilities, but pmdarima provides an auto-ARIMA function for automatic model selection. pyflux requires manual specification of ARIMA parameters but offers a wider range of time series models beyond ARIMA.

pmdarima is more suitable for users focused on ARIMA modeling with automated processes, while pyflux provides a broader set of time series models for users who need more flexibility in their analysis approach.

Pros of Orbit

• More active development and maintenance
• Broader range of forecasting models, including Bayesian structural time series
• Better documentation and examples for practical use cases

Cons of Orbit

• Steeper learning curve due to more complex models
• Potentially slower execution for simpler forecasting tasks
• Requires more dependencies and setup

Code Comparison

Pyflux example:

from pyflux import ARIMA

model = ARIMA(data=y, ar=1, ma=1, integ=1)
model.fit("MLE")
model.plot_fit()

Orbit example:

from orbit.models import DLT

model = DLT(response_col='y', date_col='ds')
model.fit(df)
predicted_df = model.predict(df)

Both libraries offer concise model creation and fitting, but Orbit's syntax is more aligned with modern data science practices, using DataFrame inputs and offering more flexibility in model configuration.

Pros of sktime

• More comprehensive and actively maintained, with regular updates and a larger community
• Offers a wider range of time series algorithms and models
• Provides a unified interface for various time series tasks, including forecasting, classification, and clustering

Cons of sktime

• Steeper learning curve due to its more complex architecture
• May be overkill for simpler time series tasks
• Requires more dependencies, potentially leading to longer setup times

Code Comparison

sktime example:

from sktime.forecasting.naive import NaiveForecaster
from sktime.datasets import load_airline

y = load_airline()
forecaster = NaiveForecaster(strategy="mean")
forecaster.fit(y)
y_pred = forecaster.predict(fh=[1,2,3])

pyflux example:

import pyflux as pf
import pandas as pd

data = pd.read_csv('data.csv')
model = pf.ARIMA(data=data, ar=1, ma=1, target='y')
model.fit()
model.plot_predict(h=10)

Both libraries offer time series forecasting capabilities, but sktime provides a more standardized interface across different algorithms, while pyflux focuses on specific time series models with a simpler API.

Pros of statsforecast

• More active development and maintenance
• Broader range of statistical forecasting models
• Better performance for large-scale forecasting tasks

Cons of statsforecast

• Less focus on Bayesian methods
• Steeper learning curve for users new to forecasting

Code comparison

statsforecast:

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)

pyflux:

from pyflux import ARIMA

model = ARIMA(data=data, ar=1, ma=1, integ=1)
model.fit("MLE")
forecasts = model.predict(h=30)

Both libraries offer time series forecasting capabilities, but statsforecast provides a more modern and scalable approach, while pyflux focuses on Bayesian methods and offers a simpler API for certain models. statsforecast is more actively maintained and suitable for large-scale forecasting tasks, while pyflux may be preferred for specific Bayesian applications or users seeking a simpler interface for certain models.