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Like the world cup finals that was shared some weeks ago? Man, I've been coding since I was a kid. I have a deep love for technology but the hype age we are liv
by taytus 8y ago
Like the world cup finals that was shared some weeks ago?
Man, I've been coding since I was a kid. I have a deep love for technology but the hype age we are living in, is just too much.
- downandout 8y agoBet on the World Cup using output from the same algorithms that got it wrong this time over 20 World Cup events, and you are likely to still come out way ahead. Real-world events have chaotic properties to them, but overall these predictions tend to be quite accurate. Casinos allow you to bet on games like roulette or blackjack because they have a mathematical expectation - a prediction - that they will come out ahead overall. Would you say that this prediction is wrong because individual players sometimes win a lot of money? You can’t look to a single event that differs from a prediction and use that as a basis for an argument that the entire model is flawed.
- icelancer 8y agoThen you should know a prediction about a game with a ton of variance with many entrants is almost always wrong. But assuming you have a perfect model, simulated over millions of attempts, it's the most correct.
- natalyarostova 8y agoI was just at the international institute for forecasters conference on Colorado as an applied practitioner. There was a lot of hype on how to best use ML/Neural-nets in time-series forecasting. Well, it was well-founded hype. It was hype by people who know there is potential there, but who also consider a 20% accuracy improvement over naive methods that were developed in the 1980s to be a cause of great success. And even then, it doesn't always work. NN/ML has had true breakthrough success in classification type problems, and fitting situations of nonlinear high-fidelity. For example, self-driving cars. But in terms of economics/demand/weather dynamics, which include much much less data, and often deal with more chaotic macro-patterns (i.e. Your data is a time-series of a few megabytes over years, rather than gigabytes over minutes from cameras), it offers much less.
- curiousgal 8y agoWhat methods are those?
- johntiger1 8y ago20% is pretty significant actually, I've read papers where they report baseline beats in the 0.1% to 1% range.