[ENH] Add LITETimeClassifier Example to Classification Notebook

[sumana-2705]

Reference Issues/PRs

Fixes #2295

What does this implement/fix? Explain your changes.

Added LITETimeClassifier example to the classification.ipynb file in the examples section, demonstrating its application to arrow_head dataset

Does your contribution introduce a new dependency? If yes, which one?

No, this contribution does not introduce any new dependencies.

Any other comments?

Please let me know if any further adjustments or enhancements are needed for the example. I am attaching the image of the cells I have changed below. Thank you for reviewing!

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I have added the following labels to this PR based on the title: [ $\color{#FEF1BE}{\textsf{enhancement}}$ ].
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[sumana-2705]

@all-contributors please add @sumana-2705 for code

[allcontributors]

@sumana-2705

I've put up a pull request to add @sumana-2705! 🎉

[hadifawaz1999]

Thank you for the contribution, just a small comment in the notebook, @MatthewMiddlehurst would like your input as well

[MatthewMiddlehurst]

Addition looks fine assuming it is fast to run, could you give a general idea on how long the cell takes for you to process?

Would also update the "Background info and references for classifiers used here" section.

[sumana-2705]

Addition looks fine assuming it is fast to run, could you give a general idea on how long the cell takes for you to process?

Would also update the "Background info and references for classifiers used here" section.

I have updated the classifier description to include runtime information and references. In a Colab environment, it took approximately 6 minutes to run.

[MatthewMiddlehurst]

That is too much unfortunately. This is obviously dependant on setup, but I would prefer around 10 seconds. The runtime is pretty long on our CI https://github.com/aeon-toolkit/aeon/actions/runs/12163243307/job/33921975615?pr=2419#step:6:741.

[sumana-2705]

It is getting reduced by reducing number of epochs, but accuracy is decreasing. 50 epchs is returning 0.35 accuracy by with 1 min of runtime, 100 epochs is returning 0.59 accuracy with 2 mins of runtime, 200 epochs is returning 0.86 accuracy with 3 mins of runtime. What should I prefer now, should I reduce epochs to reduce runtime, can you please suggest me?

[hadifawaz1999]

its okay if the number of epochs is small, you can add a comment that to really see the performance of deep learning in aeon you should use the default number of epochs, and that its reduced in this notebook for simplicity, put like 10 eoichs

[sumana-2705]

@MatthewMiddlehurst @hadifawaz1999

I have made the all required changes, if it seems okay can you please merge this PR?

[MatthewMiddlehurst]

how much time does the cell take to run now?

[sumana-2705]

Hello @hadifawaz1999

Can you please approve the changes and merge the PR if PR looks right, so that I can start workingon other issues. Thank you

[MatthewMiddlehurst]

@all-contributors please add @sumana-2705 for code

[allcontributors]

@MatthewMiddlehurst

@sumana-2705 already contributed before to code