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Differentiation to an integer degree is used to make a series stationary. However, fractional differentiation allows the exponent to be a real number. This helps to preserve memory.

To train a machine learning model, we usually need a labeled dataset. In the world of finance, this involves creating a matrix of features, $X$, and an array of labels or values, $y$. In this blog, we'll delve into various methods of labeling financial data.

You might have noticed that many financial models rely on the assumption that data points are independent and identically distributed (IID). However, this is often not the case in real-world financial applications.

In traditional settings, cross-validation is an effective tool for evaluating a machine learning model's performance. However, the complexities of financial data pose unique challenges.

When dealing with a set of training observations and outcomes, you would want to estimate a function that closely matches the true function.