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Time-Series Analysis

Bertram Scheufele


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Time-series analysis is a statistical procedure for describing the characteristics of one time series (e.g., a trend) or predicting the future development of one time series (forecasting), but it can also be used to analyze the impact of an event on a single time series (intervention analysis) and to analyze the correlations between two or more different time series (cross-correlations, transfer-function analysis). The first two applications are usually called the univariate perspective of time-series analysis; the second two are known as the multivariate perspective of time-series analysis. The univariate perspective can be compared to descriptive statistical analyses of one variable and its values for all elements of a sample distribution (→  Statistics, Descriptive ). Yet with time-series analysis the values of a certain variable are organized consecutively, i.e., in a time order. For example, one can ask 365 people ( n = 365) about the time they spend watching television and get one value for each person, i.e., 365 different values altogether. Or one can ask just one person about the time he or she spends watching television on 365 consecutive days ( t = 365). Again, one gets 365 values, but these are in a sequence and refer to one person ( n = 1). The multivariate perspective of time-series analysis can be compared to calculating correlations – with two variables – or to ... log in or subscribe to read full text

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