Auto-correlation of time signals [closed]

1. What is auto correlation in time series?
2. What is auto correlation of a signal?
3. Why is auto correlation a problem?
4. What are the consequences of auto correlation?

What is auto correlation in time series?

The term autocorrelation refers to the degree of similarity between A) a given time series, and B) a lagged version of itself, over C) successive time intervals. In other words, autocorrelation is intended to measure the relationship between a variable's present value and any past values that you may have access to.

What is auto correlation of a signal?

Autocorrelation, sometimes known as serial correlation in the discrete time case, is the correlation of a signal with a delayed copy of itself as a function of delay. Informally, it is the similarity between observations of a random variable as a function of the time lag between them.

Why is auto correlation a problem?

Autocorrelation can cause problems in conventional analyses (such as ordinary least squares regression) that assume independence of observations. In a regression analysis, autocorrelation of the regression residuals can also occur if the model is incorrectly specified.

What are the consequences of auto correlation?

If the autocorrelation is positive, standard errors tend to be smaller, and the results of the t or F tests will be inflated or biased in a positive manner. This inflation increases the Type I error rate (i.e., too often showing an effect when there actually is none).