“Granger Causality” and “Machine Learning” integration
They say motivation is what gets you started, and habit is what keeps you going. But when it comes to data science, sometimes a little bit of magic helps too. And that magic often comes in the form of Granger causality.
Following our previous articles about Granger causality — “Multivariate Granger Causality Analysis,” “Performing Granger Causality with Python: Detailed Examples,” and “Unlocking Secrets with AI: The Magic of Granger Causality in Python” — we’ve delved deep into the intricacies and applications of Granger causality. These articles explored the fundamentals of Granger causality, provided detailed examples using Python, and demonstrated the power of this technique in uncovering causal relationships in multivariate time series data. If you haven’t read them yet, I highly encourage you to check them out for a solid foundation in Granger causality.
