AMOS team contributions: forecasting, anomaly detection, decomposition, visualization

[simonselbig]

Adds the contributions from the AMOS WS2025 team (amos2025ws03-rtdip-timeseries-forecasting) to the RTDIP SDK:

• Data Manipulation (pandas & spark): Chronological sort, cyclical encoding, datetime features, lag features, MAD outlier detection, rolling statistics, and more
• Forecasting Models (spark): Prophet, LSTM, XGBoost, CatBoost, AutoGluon time series models + prediction evaluation utilities
• Decomposition (pandas & spark): Classical, STL, and MSTL decomposition methods
• Anomaly Detection (spark): IQR-based and MAD-based anomaly detection
• Visualization (matplotlib & plotly): Forecasting, decomposition, anomaly detection, and model comparison plots
• Data Sources: Azure Blob storage source

All components include tests and documentation.

Environment Changes

Added ML/forecasting dependencies: tensorflow, xgboost, plotly, prophet, sktime, catboost, autogluon.timeseries

[linux-foundation-easycla]

The committers listed above are authorized under a signed CLA.

• ✅ login: simonselbig / name: Simon Selbig (18dd377, 35eccce, 8c6a601)