tsfresh, sktime, darts, gluonts, prophet: which is the best performance?

When it comes to time series forecasting, the performance of a model can be highly dependent on the specific characteristics of the data and the problem at hand. However, here is an overview of each of the mentioned libraries, along with their strengths and common use cases:

1. blue-yonder/tsfresh:

   • Strengths: tsfresh is a feature extraction library for time series data. It automatically extracts hundreds of features from time series and can be used for both classification and regression tasks.
   • Common Use Cases: It's best used when you want to transform time series data into features for machine learning models.

2. sktime/sktime:

   • Strengths: sktime is a unified framework for machine learning with time series data. It provides a wide range of algorithms for forecasting, classification, and regression tasks, as well as tools for model selection and evaluation.
   • Common Use Cases: It's a good choice if you need a comprehensive and flexible library for various time series tasks, including forecasting, classification, and regression.

3. unit8co/darts:

   • Strengths: Darts is a Python library for easy manipulation and forecasting of time series. It offers a wide range of models, from statistical models to deep learning models, and supports both univariate and multivariate time series.
   • Common Use Cases: It's a versatile library suitable for many types of time series forecasting problems. Its integration with deep learning frameworks makes it powerful for complex forecasting tasks.

4. awslabs/gluonts:

   • Strengths: GluonTS is a library for probabilistic time series modeling, built on top of MXNet. It includes state-of-the-art deep learning models and supports probabilistic forecasting.
   • Common Use Cases: It's highly recommended for tasks that benefit from probabilistic forecasting and deep learning models, especially in cases where you need to handle uncertainty in predictions.

5. facebook/prophet:

   • Strengths: Prophet is designed for producing high-quality forecasts with a simple interface. It automatically handles missing data and outliers, and includes components for seasonality and holidays.
   • Common Use Cases: It's often used for business forecasting tasks, such as predicting sales, website traffic, and other business metrics. It's particularly strong in handling daily and weekly seasonality.

Performance Comparison

While each library has its strengths, some general observations about their performance are as follows:

• GluonTS: Often considered one of the most powerful libraries for time series forecasting due to its deep learning capabilities and probabilistic forecasting features. It performs particularly well on complex datasets with a lot of variability.

• Prophet: Known for its simplicity and effectiveness in business forecasting applications. It may not perform as well as deep learning models on very complex datasets but is highly user-friendly and good at capturing seasonality and trends.

• Darts: Provides a balance between traditional statistical models and deep learning models. It's a good choice if you need flexibility and a wide range of models to experiment with.

• sktime: Offers a comprehensive set of tools for different time series tasks and is especially useful if you need to perform not just forecasting but also classification and regression on time series data.

• tsfresh: While not a forecasting library per se, it is excellent for feature extraction from time series data, which can then be used to improve the performance of other machine learning models.

Recommendations

• For state-of-the-art performance on complex forecasting tasks, GluonTS is highly recommended.
• For business applications with strong seasonality and trend components, Prophet is a solid choice.
• For a versatile and comprehensive toolkit, sktime or Darts would be good options.
• For feature extraction and preprocessing, tsfresh can be very useful in conjunction with other machine learning models.

Ultimately, the best choice depends on your specific use case, data characteristics, and the type of forecasting problem you're dealing with. It might be worth experimenting with multiple libraries to see which one performs best for your particular scenario.