TIME SERIES CLASSIFICATION | Let's Learn from My Mistakes 🌱

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Published 2024-01-10

In this video, we look at some approaches that could not give us high AUC scores. I also share my opinion about why they don't work really well. After editing this video, I feel like at least one of the lessons might sound like an obvious lesson. But, I guess, we can just forget about it sometimes when we are searching for the best models.

Thank you so much for watching!

00:00 Intro
00:44 Description of the data and data processing
04:38 Approach 1: Shapelets and gradient boosting
12:42 Approach 2: Shapelets and logistic regression
13:50 Approach 3: Shapelets and neural network
15:48 Approach 4: LSTM
22:11 Approach 5: LSTM with more features
27:58 Takeaways and a question

Source code: github.com/stephanielees/time-series-classificatio…

#timeseries #classification #neuralnetworks #datascience #datamining #deeplearning #python #kaggle

All Comments (3)

@misakostic72 2 months ago

Excellent video. I thoroughly enjoyed commentary on "failures", and analysis of underlying causes. Thank you for this great content.
@nishmithaur8743 5 months ago

I stumbled upon your tutorials recently and really enjoyed the content! You provide clear and concise explanations of complex topics. While I found your content valuable, I noticed the audio could be improved in some videos. Sometimes your voice echoes a bit, which might make it harder for some viewers to hear everything clearly. Overall, I'm impressed with your tutorials and excited to see more from you in the future. Keep up the great work!