![]() They insert the data into a statistical modeling program and perform an autocorrelation analysis. They assess the strength of the correlation between these different versions to uncover trends and patterns that allow them to evaluate the strength of the relationship between two or more variables.įor example, a weather scientist might use this function when analyzing the minimum daily temperature recorded in a city using a data set from the last 10 years. To use this function, the analyst uses the same time series and compares it against a lagged version of itself over one or more time periods. This function allows the analyst to compare the current value of a data set to its past value. ![]() The autocorrelation function is a statistical representation used to analyze the degree of similarity between a time series and a lagged version of itself. In this article, we explain what the autocorrelation function is, when to use it and how to apply it in four steps. Understanding what autocorrelation functions are, why they're important and how they're used in various industries can help you know how and when to apply them in your own work. This function allow analysts to study how patterns within a single time series correlate when compared against lagged versions of themselves one or more times. When performing statistical assessments of time series data, one important concept to understand is the autocorrelation function.
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