Predict the future with Machine Learning

The article seems to be a decent introduction article that shows what Machine Learning is about (which is great) and shows how it can POSSIBLY be applied to forecasting and prediction. However, I think it would be even better if there was a simple example or two with each method being applied and showing different outcomes and then the significance of each methodology through those examples.

Also, I'd like to add a comment to this.

This article is great when you look at it from the Machine Learning perspective. However, when you look at it from a forecasting perspective it only shows a very small portion of what forecasting/predicting really is.

Algorithms you develop through machine learning is something known as a black-box model. You know that the input data and the output data you're matching up with are related somehow, but don't know exactly how they're matched up. That relationship is established based on a performance index determined from a trial-and-error method (of course depending on what actual method you use).

There are different methods available such as ARMA and ARIMA based models. In regards to physical science, there are models that focus on the physical interaction between the input data to simulate what is happening inside the system. ML methods are simply just a taste of other methods available.

Regards to programming use (as I'm sure most of you folks here are used to), ML is a good tool to use for forecasting if you're really interested in it. But just like any forecasting model you use, you should probably determine the performance of your model based on not one index but multiple indices which consider different parts of your "needs". Percent accuracy only shows how accurate you are, you should probably also consider how frequently you're over-estimating vs under-estimating, how many series of overestimation there is, etc. The most important one though in my books is bias correction. When working with ML Algorithms most of them do not consider for bias. So you, as the modeller need to prepare for bias correction. However this article kinda glossed over it by saying "right amount of data" and "combination of data" (which I understand is an introductory post, but I think this is very important).

Maybe look into applying a method like the K-Fold Cross Validation to make sure the final output parameters aren't AS biased. It really depends on the modeller and the model and your performance indices you use.

Stopped here. Moving on.

[1] https://www.amazon.com/Real-Analysis-Lifesaver-Understand-Pr...

I think time invested into studying real analysis pays off because then you can later study measure theory, functional analysis and more advanced probability to deal with curse of dimensionality and whatnot.

edit: I started studying the book linked above starting from chapter 4 since the first 3 chapters are familiar from discrete math. Then did chapter 5, skimmed chapters 6(little linear algebra), 7, 8 (most "transition to higher math" books contain this stuff) and am currently in chapter 9.

Yes, simulators as sources of sensory input to get similar experience are used, for example, to train airline pilots. Nevertheless, there is no airline which train its pilots on a simulator only. Algorithm, like a pilot, would learn a simulation, not reality.