For example, the temperatures on different days in a month are autocorrelated. Autocorrelation analysis measures the relationship of the observations between the different points in time, and thus seeks a pattern or trend over the time series. In many cases, the value of a variable at a point in time is related to the value of it at a previous point in time. Autocorrelation gives information about the trend of a set of historical data so that it can be useful in the technical analysis for the equity market.A value between 0 and 1 represents positive autocorrelation. A value between -1 and 0 represents negative autocorrelation. The value of autocorrelation ranges from -1 to 1.Autocorrelation, also known as serial correlation, refers to the degree of correlation of the same variables between two successive time intervals.
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