This pull request introduces several improvements and bug fixes to the synthetic time series generation system, focusing on greater configurability, more robust series generation, and enhanced code clarity. The most significant changes are the addition of an exponential trend toggle, improved handling of NaN values during series generation, and clearer separation and documentation of trend, seasonal, and noise components.

Time Series Generation Improvements:

• Added an exp_trend parameter to the time series generator class (ForecastPFNExcitation), allowing users to enable or disable exponential trend components in generated series. The exponential scale is now only applied if this flag is set. [1] [2] [3]
• Modified the construction of trend, seasonal, and noise components in the make_series function to be more explicit and better documented, clarifying the order and method by which these components are combined.

Robustness and Bug Fixes:

• Improved the generate method to check for NaN values in generated series and retry generation as needed, ensuring that the final output does not contain NaNs.

Configurability and Scaling:

• Adjusted scaling of exponential and offset components in series configuration to depend on the exp_trend flag and timescale, improving the realism and flexibility of generated data. [1] [2]

Code Clarity and Documentation:

• Added comments and docstrings to clarify the logic behind generating transitions between time series segments and the construction of blended series. [1] [2]

Notebook and Testing Updates:

• Updated the example Jupyter notebook 2-complex_systems.ipynb to use the aeon kernel and Python 3.11.9, and removed an empty code cell. [1] [2]
• Minor formatting improvement in test file.