Time Series Classification Using Deep Learning - Part 1 [Blog Post]

Yesterday, I published this blog post:

Time Series Classification Using Deep Learning - Part 1.

Thank you @jeremy for retweeting it and pointing out the broken link. That was also my first tweet :grimacing:

I would like to thank all the fastai members who liked the tweet, and also retweeted it.

Thank you @jcatanza and @lgvaz for being my 2 first followers! before even starting tweeting.

As a new Twitter user, I would like to kindly ask you to follow me @ai_fast_track. I will post there the sequel of this blog post.


Immediately followed :wink: (late to the party). Congrats! :confetti_ball:

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I’m joining the party too. Great article!

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Thank you Zach. You are not at all late to the party (it’s still on for me :smile:). You are the Speedy of fastai forum :+1:

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Thank you @Tendo. BTW, Great post Common decorators in fastai2 source code

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Great blog post! Looking forward to part 2! :slight_smile:

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Thank you @stefan-ai. Hopefully soon!

Great work @farid!!

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Thank you @vrodriguezf for retweeting and following!

Thank you this is amazing! :star_struck:

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Thank you @dcsw, and welcome to the fastai community!

An awesome and quality blog post. I am very excited and pumped for the coming posts in the series. Something which are gonna really really helpful. Could you please tell, are there any regression datasets under URLs_TS ? Does the same ts_learner with InceptionTime architecture handles the regression task and how can it be done with an example(4 lines of code)?
Thank you & Stay safe.

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Thank you for your very positive comment! I’m glad that you find it interesting.

URLs_TS only includes classification datasets. All the URLs_TS datasets come from the UEA & UCR Time Series Classification Repository.

I didn’t find yet any regression datasets stored as .arff files (the format supported by timeseries package for now).

Not yet but I’m planning to add that feature hopefully soon.

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Thank you so much for your clarifications. Will be waiting for the next post which is what I am very eager for ( knowledge and understanding for a library building procedure)…