Using Collatz Conjectures, ML5 and Chart.js to encode stocks prices and try to use them to predict stock prices for a very short interval of time and with a low precision.
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β methods for scaling, stretching datasets, concatenating (2.2)
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β MSE function to measure distance of a Collatz series for a specific n to a certain stock (2.3)
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β neural network which is able to approximate a series of factors with length of the associated Collatz series to reduce MSE and model a stock (3.1)
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β maybe using some tensorflow to model stocks (3.2)
In this section I am going to present the proccess of implementing the certain features from "Future Features".
In this projectβs context a dataset is a simple Collatz series. I want to offer some methods which can be used to manipulate the Collatz series as a whole. Thus the methods will allow the user to scale, stretch and add Collatz series.
This method will be used to add two Collatz series element-wise. The implementation is very simple:
add(coll) {
return this.result.map((datapoint, index) => datapoint + coll[index]);
}
It simply maps the first series to the sum of the picked element and the element at the same index in the other series element-wise.