Pros of sktime

• More comprehensive and actively maintained time series toolbox
• Larger community and contributor base
• Extensive documentation and examples

Cons of sktime

• Potentially more complex API due to broader feature set
• May have a steeper learning curve for beginners

Code Comparison

sktime:

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])

sktime>:

# No code comparison available as sktime> is not a separate repository
# It appears to be a typo or misunderstanding in the original question

Summary

sktime is a well-established time series analysis and forecasting library for Python. It offers a wide range of algorithms and tools for various time series tasks. The repository is actively maintained and has a growing community of contributors and users. While it provides extensive functionality, this breadth may result in a more complex API and a steeper learning curve for newcomers.

The comparison to "sktime>" is not applicable, as it appears to be a typo or misunderstanding. There is no separate repository with that name. sktime is the primary and only repository for the sktime project.

Pros of statsmodels

• More comprehensive statistical modeling capabilities, including econometrics and advanced regression techniques
• Longer history and more established in the scientific community
• Extensive documentation and examples for various statistical methods

Cons of statsmodels

• Steeper learning curve due to its broader scope and more complex API
• Less focus on time series forecasting compared to sktime
• Slower development cycle and less frequent updates

Code comparison

statsmodels:

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

sktime:

from sktime.forecasting.arima import ARIMA
forecaster = ARIMA()
forecaster.fit(y)
predictions = forecaster.predict(fh=[1, 2, 3])

Summary

statsmodels is a comprehensive statistical library with a wide range of modeling capabilities, while sktime focuses specifically on time series analysis and forecasting. statsmodels offers more advanced statistical techniques but has a steeper learning curve, whereas sktime provides a more user-friendly interface for time series tasks. The choice between the two depends on the specific requirements of your project and your familiarity with statistical concepts.

Pros of Prophet

• Designed specifically for business forecasting with built-in handling of holidays and seasonal effects
• User-friendly interface with automatic hyperparameter tuning
• Robust to missing data and outliers

Cons of Prophet

• Limited to univariate time series forecasting
• Less flexible for custom model architectures compared to sktime
• May struggle with complex, non-linear patterns in data

Code Comparison

Prophet:

from fbprophet import Prophet

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

sktime:

from sktime.forecasting.arima import AutoARIMA

forecaster = AutoARIMA()
forecaster.fit(y_train)
y_pred = forecaster.predict(fh=fh)

Prophet focuses on simplicity and ease of use, while sktime offers a more comprehensive toolkit for various time series tasks. Prophet excels in business forecasting scenarios with clear seasonality and holiday effects, whereas sktime provides greater flexibility for different types of time series analysis and forecasting methods.

Pros of pmdarima

• Specialized focus on ARIMA models, offering advanced features and optimizations
• Includes automated model selection and hyperparameter tuning
• Provides comprehensive documentation and examples for ARIMA-specific use cases

Cons of pmdarima

• Limited to ARIMA models, lacking the broader range of time series algorithms found in sktime
• Less flexibility for handling complex time series tasks beyond ARIMA modeling
• Smaller community and ecosystem compared to sktime

Code Comparison

pmdarima:

from pmdarima import auto_arima

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

sktime:

from sktime.forecasting.arima import AutoARIMA

forecaster = AutoARIMA(sp=12)
forecaster.fit(y)
forecast = forecaster.predict(fh=[1,2,3,4,5])

Both libraries offer auto ARIMA functionality, but pmdarima's implementation is more specialized and may offer additional ARIMA-specific options. sktime provides a more consistent API across various time series algorithms, making it easier to switch between different forecasting methods.

Pros of tslearn

• Simpler API and easier to get started for beginners
• Focuses specifically on time series tasks, potentially leading to more optimized implementations
• Includes some unique algorithms not found in sktime, like GAK (Global Alignment Kernel)

Cons of tslearn

• Smaller community and less frequent updates compared to sktime
• More limited in scope, focusing mainly on clustering and classification tasks
• Less integration with the broader scikit-learn ecosystem

Code Comparison

tslearn example:

from tslearn.clustering import TimeSeriesKMeans
kmeans = TimeSeriesKMeans(n_clusters=3, metric="dtw")
kmeans.fit(X_train)

sktime example:

from sktime.clustering.kmeans import TimeSeriesKMeans
kmeans = TimeSeriesKMeans(n_clusters=3, distance="dtw")
kmeans.fit(X_train)

Both libraries offer similar functionality for time series clustering, with slight differences in API design. sktime generally follows scikit-learn conventions more closely, while tslearn has its own unique API style. sktime provides a broader range of functionality beyond just clustering and classification, making it more versatile for various time series tasks.

Pros of pyflux

• Focuses specifically on probabilistic time series modeling and Bayesian inference
• Includes specialized models like GARCH for volatility forecasting
• Provides built-in plotting and diagnostics for model evaluation

Cons of pyflux

• Less actively maintained compared to sktime (last update in 2018)
• More limited in scope, primarily for financial time series analysis
• Smaller community and fewer contributors

Code Comparison

pyflux example:

import pyflux as pf
model = pf.ARIMA(data=y, ar=1, ma=1, family=pf.Normal())
model.fit()
model.plot_fit()

sktime example:

from sktime.forecasting.arima import ARIMA
forecaster = ARIMA(order=(1,0,1))
forecaster.fit(y)
y_pred = forecaster.predict(fh=[1,2,3])

Both libraries offer ARIMA modeling, but pyflux provides more built-in visualization tools, while sktime offers a broader range of time series algorithms and a more consistent API across different model types. sktime also integrates better with the wider scikit-learn ecosystem, making it more versatile for general machine learning tasks involving time series data.