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picardythird t1_iyj138u wrote

IIRC there was a recent paper that demonstrated how almost all deep learning approaches for time series forecasting use flawed evaluation procedures, resulting in misleading claims about performance and terrible out-of-distribution performance.

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whatsafrigger t1_iyjaub9 wrote

It's so so so important to set up good experiments with solid baselines and comparisons to other methods.

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notdelet t1_iyjhvqd wrote

If you use a flawed evaluation procedure, does a solid baseline do you any good?

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Ulfgardleo t1_iylr164 wrote

The "and" in the post you replied to was a logical "and". The best evaluation procedure does not help if you use poor, underperforming baselines.

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csreid t1_iykq7xn wrote

And it's sometimes kinda hard to realize you're doing a bad job, especially if your bunk experiments give good results

I didn't have a ton of guidance when I was writing my thesis (so, my first actual research work) and was so disheartened when I realized my excellent groundbreaking results were actually just from bad experimental setup.

Still published tho! ^^jk

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Pikalima t1_iylah8s wrote

Sometimes I consider retracting my very first paper because of this.

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maxToTheJ t1_iyjw8b8 wrote

A lot of people are doing hyperopt with how they set up their experiment to get better results so that they get in prestigious conferences.

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kraegarthegreat t1_iyolbmo wrote

This PLAGUES my research.

The amount of detail that most papers provide about their statistical methods used as a baseline is not enough to replicate. "We outperformed ARIMA". Didn't provide values, etc.

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uoftsuxalot t1_iyjrkll wrote

I would say forecasting in general is bs.

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ragamufin t1_iyl761r wrote

I’ve been doing it for a decade+ and I’m inclined to agree but it pays well and there’s no shortage of buyers. Even straight up named a model GIPSy once with a crystal ball logo, had a pretty good run.

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uoftsuxalot t1_iyshw3h wrote

Lol, I'm minus 7 and you're positive 7 karma yet agreeing 😂. Reddit is so stupid sometimes

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