Machine Learning Quick Reference

Granger causality

In time series, we typically use univariate data. That is, we use a single series to predict its future values. Let's say that we are studying Google's stock price data, and we are asked to forecast the future values of stock prices. In this case, we will need historic data of Google's stock prices. Based on that, we will make predictions.

However, at times, we need multiple time series to make a forecast. But why is it that we need multiple time series? Any guesses?

The following graph shows Google's stock price data:

The answer is that we need to understand and explore the relationship between multiple time series as this ...