What a Drunk Man Can Teach Us About Time Series Forecasting

Autocorrelation & The Random Walk explained with a drunk man 🍺

Let me illustrate this statistical concept with an example we can all visualize.

Imagine a drunk man wandering a city. His steps are completely random and unpredictable.

Here’s the intuition:

– His current position is completely tied to his previous position

– We know where he is RIGHT NOW, but have no idea where he’ll be in the next minute

The statistical insight:

In a random walk, the current position is highly correlated with the previous position, but the changes in position (the steps) are completely random & uncorrelated.

This is why random walks are so tricky to forecast!

Part 2: Time Series Forecasting: Build a Baseline & Understand the Random Walk

Would love to hear your thoughts, feedback about this topic