Convert a Time Series into a Classification Problem
This article describes an example project in the context of stock trading. Can we predict with an AI model whether a stock goes up or down the next day? It is precisely the issue that we examine in this article.
We compare the classification algorithms XGBoost, Random Forest and Logistic Classifier. In addition, the focus of the article is on data preparation. How can we transform the data so that the models can process it?
But before we start, we have to think about the problem statement and the financial data we need. In this article, we will follow the CRISP-DM process model so that we have a structured approach to solving the business case. CRISP-DM is particularly suitable for potential analyses and is often used in industry to structure data science projects.
As a data scientist, the first question you should ask yourself is, where do I get my data from? We will use the Python package openbb. The package bundles some data sources from the financial sector in a convenient way.
So let’s get started!
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