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 one year is likely to lead to overestimates in the succeeding years.

The time series analysis data arises in lots of different scientific applications and 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 whether the data is random or not. For that we can use correlogram. It can help provide answers to questions such as: Is the data random? Is this time series data a white noise signal? Is it sinusoidal? Is it autoregressive? What...