Inverted Transformers Are Effective for Time Series Forecasting

Re:Inverted Transformers Are Effective for Time Series Forec... by Galanwe on Thursday October 12, 2023 @04:47AM (#37854860)

What they describe looks like a single predictor. You can't create a strategy with a single predictor, unless it's incredibly predictive. 99% of the time, a predictor cannot beat its transaction costs alone.

You need to combine hundreds of such predictors to be able to beat costs and have a net profitable strategy.

We have a saying in French that you need a lot of rivers to create a sea.

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Re:Inverted Transformers Are Effective for Time Series Forec... by benreesman on Thursday October 12, 2023 @06:33AM (#37855444)

So the group involved veterans from like Knight and DRW and stuff: we understood the model of combining lots of small signals with a low-latency regression.

We were trying to learn those signals as opposed to sweat-shop them.

But the broader point holds: signal isn’t alpha.

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Re:Inverted Transformers Are Effective for Time Series Forec... by nyrikki on Thursday October 12, 2023 @11:11AM (#37858327)

Wasn't the US housing crisis of the late 2000s caused by that 99% threshold?

Not in finance at all but I do use reverse Kalman filters, to which this seems similar in core concepts.

While reverse Kalman filters are incredibly helpful in reducing cloud spends by predicting when to auto scale, you still have to have metrics to quickly recover from mistakes.

Based only on tech interviews with HFT companies, I would assume someone could predict your moves using these methods based on historical data.

But perhaps I am just too risk adverse or am missing the core concept.

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Re:Inverted Transformers Are Effective for Time Series Forec... by benreesman on Thursday October 12, 2023 @02:58PM (#37861489)

You might be referring to the Gaussian coppula bullshit Dave Li did? [1]

[1] https://en.m.wikipedia.org/wiki/David_X._Li

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