1. Tree-based vs Time Series: One of the major differences between XGBoost and sktime is the type of data they are designed for. XGBoost is primarily designed for tree-based models, which work well for tabular or structured datasets. On the other hand, sktime is specifically designed for time series data, which is commonly found in areas such as finance, economics, and environmental sciences. Sktime provides various algorithms and tools specifically tailored for time series analysis.

2. Feature Engineering: XGBoost and sktime also differ in how they handle feature engineering. XGBoost requires manual feature engineering, where the user needs to engineer and select appropriate features for the model. Sktime, on the other hand, provides built-in feature extraction and transformation methods specifically designed for time series data. Sktime also offers advanced feature selection and generation techniques specifically tailored for time series forecasting tasks.

3. Ensemble Methods: Another difference between XGBoost and sktime is in their approach to ensemble methods. XGBoost is known for its excellent performance in building ensembles of decision trees. It uses boosting techniques to combine weak learners and improve the overall predictive power. Sktime, on the other hand, focuses more on ensemble approaches for time series forecasting. It provides ensemble methods specialized for time series tasks, such as the temporal aggregation ensemble, which combines multiple forecasts produced by different algorithms.

4. Support for Different Algorithms: While XGBoost is primarily focused on tree-based models, sktime offers a broader range of algorithms for time series analysis. Sktime includes not only tree-based algorithms like random forest and gradient boosting, but also other algorithms such as k-nearest neighbors, support vector machines, and various time series specific models. This makes sktime a versatile library for time series analysis, catering to different modeling needs.

5. Model Evaluation: XGBoost and sktime also differ in how they evaluate models. XGBoost provides various evaluation metrics for classification and regression tasks, such as accuracy, precision, recall, and mean squared error. Sktime, on the other hand, provides specialized evaluation metrics specifically designed for time series forecasting tasks. These metrics include mean absolute error, root mean squared error, and more. Sktime also offers additional evaluation techniques, such as backtesting, which allows for evaluating models on rolling windows of data to simulate real-world forecasting scenarios.

6. Integration with Other Libraries: XGBoost and sktime also differ in their integration with other libraries and ecosystems. XGBoost is widely supported and integrated with popular machine learning libraries like scikit-learn and can easily be used within existing workflows. Sktime, on the other hand, is a standalone library that is built on top of scikit-learn. It integrates seamlessly with scikit-learn's API, allowing users to leverage sktime's time series-specific functionality within the familiar scikit-learn ecosystem.

In summary, XGBoost and sktime differ in their focus on tree-based models vs time series analysis, their approach to feature engineering and ensemble methods, the range of algorithms they support, their model evaluation metrics, and their integration with other libraries.