Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis
Throughout this book, we have learned about various methods we can use to interpret supervised learning models. They can be quite effective at assessing models while also uncovering their most influential predictors and their hidden interactions. But as the term supervised learning suggests, these methods can only leverage known samples and permutations based on these known samples’ distributions. However, when these samples represent the past, things can get tricky! As the Nobel laureate in physics Niels Bohr famously quipped, “Prediction is very difficult, especially if it’s about the future.”
Indeed, when you see data points fluctuating in a time series, they may appear to be rhythmically dancing in a predictable pattern – at least in the best-case scenarios. Like a dancer moving to a beat, every repetitive movement (or frequency) can be attributed to seasonal patterns...