Time series forecasting has proven useful in a number of different domains from weather to health monitoring. Sure you can easily over fit on the training data, but in general that's a data source/input problem where you need many high quality data sources to find the signal in the noise.
replyThe world is chaotic sure, but there are still truths to be found in noisy time series data; saying that the world is too random to be knowable is a bit dismissive, no?
I agree when it comes to highly niche applications with a generous SNR.
replyUniversal models though?
And I haven't even mentioned the fact that en mass forecasting ITSELF may influence the subject of forecasting.
> time forecasting is bullshit
replyfor a model to be useful, it doesnt need to capture the behavior of a system. It only needs to capture signals which can be useful. For example, for a biased coin toss, a model is already useful if it can predict a little better than random.
Yeah all weather forecasts are just magic
replyWeather forecasts are notoriously iffy, and accuracy drops with time, but we understand the physics behind it (to a large extent). There's also a lot of fine-grained data available. For some arbitrary time series, there's only one data sequence, and the model is unknown. Extrapolation then becomes a lot more magical.
replyWhether forecasting is simple: it either rains or it doesn’t. 50/50 probability!
replyAnd JPG doesn't work either..
reply> Shannon would tell us that time forecasting is bullshit
replyIf you're trying to forecast random data, then yes, it's bullshit. Otherwise you have a chance.
But, if you don't have the information required for a forecast, then the outcome can look random. We know the physics needed to predict the outcome of a dice throw, but, since to predict the outcome you would need a lot of information that you don't have, the output is random to you.
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