21–30 of 43 posts
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Why? That is what everybody uses. What do you use?
L1-regularized autoregressive features, holiday dummies, Fourier terms (if suitable in combination) yield lower test errors, are faster in training, and easier to cross-validate than Prophet.
For those interested in transformers with time series, I recommend reading this paper: https://arxiv.org/pdf/2205.13504. There is also plenty of other research showing that transformers-based time series models generally underperform much simpler alternatives like boosted trees.
After looking further it seems like this startup is both trying to publish academic research promoting these models as well as selling it to businesses, which seems like a conflict of interest to me.
I'm not a fan of this blog post as it tries to pass off a method that's not accepted as a good or standard time series methodology (graph transformers) as though it were a norm. Transformers perform poorly on time series, and graph deep learning performs poorly for tasks that don't have real behaviorial/physical edges (physical space/molecules/social graphs etc), so it's unclear why combining them would produce anyth…
> After looking further it seems like this startup is both trying to publish academic research promoting these models as well as selling it to businesses, which seems like a conflict of interest to me.
is this a general rule of thumb that one should not use the same organization to publish research and pursue commercialization generally?
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Prophet is great and we use it for multiple models in production at work. Our industry has tons of weird holidays and seasonality and prophet handles that extremely well.
Earlier quoted context omitted.
For such strong and personal statement I have to ask why.
If you arrived into, say, London and googled "Best fish and chips" would you believe that the top result gives you the meal that you're after?
I'm not a fan of this blog post as it tries to pass off a method that's not accepted as a good or standard time series methodology (graph transformers) as though it were a norm. Transformers perform poorly on time series, and graph deep learning performs poorly for tasks that don't have real behaviorial/physical edges (physical space/molecules/social graphs etc), so it's unclear why combining them would produce anyth…
I'm not a fan of this blog post as it tries to pass off a method that's not accepted as a good or standard time series methodology (graph transformers) as though it were a norm. Transformers perform poorly on time series, and graph deep learning performs poorly for tasks that don't have real behaviorial/physical edges (physical space/molecules/social graphs etc), so it's unclear why combining them would produce anyth…
Earlier quoted context omitted.
For such strong and personal statement I have to ask why.
If you arrived into, say, London and googled "Best fish and chips" would you believe that the top result gives you the meal that you're after?