Importance of autocorrelation

Autocorrelation represents the degree of similarity between a given time series and a lagged (that is, delayed in time) version of itself over successive time intervals. It occurs in time series studies when the errors associated with a given time period carry over into future time periods. For example, if we are predicting the growth of stock dividends, an overestimate in 1 year is likely to lead to overestimates in the succeeding years.

The time series analysis data arise in lots of different scientific applications and in lots of financial processes. Some of the examples include: generated reports of financial performance, prices over time, computing volatility, and others.

If we are analyzing unknown data, autocorrelation can help us detect if the data is random or not. For that, we can use a correlogram. It can help provide answers to questions such as: is the data random, is this time series data a white noise, is it sinusoidal, is it autoregressive, what...