Pros of statsmodels

• Comprehensive statistical library with a wide range of models and tests
• Well-established and extensively documented
• Integrates seamlessly with other scientific Python libraries

Cons of statsmodels

• Steeper learning curve for beginners
• Can be slower for certain operations compared to specialized libraries
• Less focused on time series analysis specifically

Code Comparison

statsmodels:

import statsmodels.api as sm
model = sm.tsa.ARIMA(data, order=(1,1,1))
results = model.fit()
forecast = results.forecast(steps=5)

pmdarima:

from pmdarima import auto_arima
model = auto_arima(data, start_p=1, start_q=1, max_p=5, max_q=5)
forecast = model.predict(n_periods=5)

Key Differences

• statsmodels offers a broader range of statistical models and tests
• pmdarima focuses specifically on time series analysis and forecasting
• pmdarima provides automated model selection with auto_arima
• statsmodels requires manual specification of ARIMA parameters
• pmdarima has a more user-friendly API for time series tasks

Both libraries have their strengths, with statsmodels being more comprehensive and pmdarima offering specialized time series functionality. The choice between them depends on the specific requirements of your project and your familiarity with time series analysis.

Pros of Prophet

• User-friendly interface with automatic handling of seasonality and holidays
• Robust to missing data and outliers
• Supports multiple seasonalities and custom seasonalities

Cons of Prophet

• Less flexible for complex time series patterns
• Can be slower for large datasets
• Limited options for fine-tuning model parameters

Code Comparison

Prophet:

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

pmdarima:

from pmdarima import auto_arima
model = auto_arima(y, seasonal=True, m=12)
forecast = model.predict(n_periods=365)

Key Differences

• Prophet focuses on ease of use and automatic forecasting
• pmdarima offers more control over model selection and parameters
• Prophet handles seasonality and holidays out-of-the-box
• pmdarima provides a wider range of ARIMA-based models

Use Cases

Prophet is ideal for:

• Quick forecasting with minimal configuration
• Handling multiple seasonalities and holiday effects

pmdarima is better suited for:

• More complex time series analysis
• Fine-tuning model parameters
• Exploring various ARIMA-based models

Both libraries have their strengths, and the choice depends on the specific requirements of the forecasting task at hand.

Pros of pyflux

• Offers a wider range of time series models, including ARIMA, GARCH, and state space models
• Provides Bayesian inference capabilities for more robust uncertainty quantification
• Includes built-in plotting functions for easy visualization of results

Cons of pyflux

• Less actively maintained, with fewer recent updates compared to pmdarima
• Documentation is less comprehensive and may be outdated in some areas
• Smaller community and fewer contributors, potentially leading to slower bug fixes and feature additions

Code Comparison

pyflux example:

import pyflux as pf
model = pf.ARIMA(data=df['column'], ar=1, ma=1, integ=1)
result = model.fit()
forecast = model.predict(h=5)

pmdarima example:

from pmdarima import auto_arima
model = auto_arima(df['column'], start_p=1, start_q=1, d=1, max_p=5, max_q=5)
forecast = model.predict(n_periods=5)

Both libraries offer ARIMA modeling capabilities, but pyflux provides a more explicit model specification, while pmdarima's auto_arima function automatically selects the best parameters. pmdarima focuses on ease of use and automation, while pyflux offers more flexibility in model customization.

Pros of Orbit

• Supports Bayesian modeling for time series forecasting
• Handles multiple seasonality patterns
• Provides built-in visualization tools for model diagnostics

Cons of Orbit

• Steeper learning curve due to its Bayesian approach
• More computationally intensive, especially for large datasets
• Less extensive documentation compared to pmdarima

Code Comparison

pmdarima example:

from pmdarima import auto_arima

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

Orbit example:

from orbit.models import DLT

model = DLT(response_col='y', date_col='ds', seasonality=52)
model.fit(df)
prediction = model.predict(df)

Key Differences

• pmdarima focuses on traditional ARIMA models, while Orbit emphasizes Bayesian structural time series
• Orbit offers more flexibility in modeling complex seasonality patterns
• pmdarima provides automated model selection (auto_arima), whereas Orbit requires more manual configuration
• Orbit integrates well with Pandas DataFrames, while pmdarima works primarily with numpy arrays

Both libraries have their strengths, with pmdarima being more accessible for beginners and Orbit offering advanced capabilities for complex time series modeling.

Pros of sktime

• Broader scope: Covers various time series tasks beyond forecasting, including classification and regression
• Scikit-learn compatible: Integrates well with the scikit-learn ecosystem
• Extensive functionality: Offers a wide range of algorithms and tools for time series analysis

Cons of sktime

• Steeper learning curve: More complex API due to its broader scope
• Less specialized: May not offer as deep functionality for specific ARIMA-related tasks

Code Comparison

sktime example:

from sktime.forecasting.arima import ARIMA
forecaster = ARIMA(order=(1, 1, 1), seasonal_order=(1, 1, 1, 12))
forecaster.fit(y_train)
y_pred = forecaster.predict(fh=[1, 2, 3])

pmdarima example:

from pmdarima import auto_arima
model = auto_arima(y_train, seasonal=True, m=12)
forecasts = model.predict(n_periods=3)

The main difference is that pmdarima offers automatic ARIMA model selection with auto_arima, while sktime requires manual specification of ARIMA parameters. However, sktime provides a more consistent interface across different time series tasks and algorithms.

Pros of statsforecast

• Faster execution times, especially for large datasets
• Supports a wider range of statistical forecasting models
• Designed for scalability and high-performance computing

Cons of statsforecast

• Less mature and potentially less stable than pmdarima
• May have a steeper learning curve for users familiar with pmdarima
• Documentation might not be as comprehensive as pmdarima's

Code Comparison

statsforecast:

from statsforecast import StatsForecast
from statsforecast.models import AutoARIMA

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

pmdarima:

from pmdarima import auto_arima

model = auto_arima(y, seasonal=True, m=12)
forecasts = model.predict(n_periods=30)

Both libraries offer AutoARIMA functionality, but statsforecast is designed for handling multiple time series simultaneously, while pmdarima focuses on single time series analysis. statsforecast's API is more oriented towards batch processing, which can be advantageous for large-scale forecasting tasks. However, pmdarima's syntax might be more intuitive for users familiar with traditional statistical modeling approaches.