Autocorrelation Coefficient

Autocorrelation Coefficient is a statistical measure that calculates the strength of the relationship between a variable's current value and its past values. It is used to identify patterns or relationships between time series data points. The coefficient ranges from -1 to 1, where -1 indicates a perfect negative correlation, 0 indicates no correlation, and 1 indicates a perfect positive correlation. A high autocorrelation coefficient indicates that the variable's past values can be used to predict its future values with a high degree of accuracy. Autocorrelation is commonly used in time series analysis, signal processing, and machine learning algorithms such as ARIMA and LSTM.
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