What is autocorrelation in time series book?
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Autocorrelation in time series refers to the correlation between a time series and a lagged version of itself. It is a fundamental concept in time series analysis and is used to understand the structure and patterns within the data. Autocorrelation can help identify relationships between variables, detect non-linear relationships, and test hypotheses about the nature and pattern of relationships. It is particularly useful in financial forecasting . Autocorrelation functions, such as the sample autocorrelation function (ACF), are commonly used to measure and analyze autocorrelation. The ACF provides a method for testing hypotheses and understanding the change in response or behavioral patterns over time . Other measures of autocorrelation, such as distance covariance/correlation and discrepancy-based autocorrelation, have also been introduced and shown to have advantages in certain contexts .
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8 Citations | Autocorrelation in time series analysis refers to the measure of correlation between observations at different time points within the same series. |
| Autocorrelation in time series refers to the correlation between a time series and a lagged version of itself. | |
18 Jul 2010 | The paper discusses a concept for autocorrelation based on Hermann Weyl's discrepancy norm for time series analysis. It is shown that this autocorrelation concept has better discriminative power for high-frequency signals, such as those encountered in a financial context. |
| The paper provides information on the properties of the sample autocorrelation function (ACF) for a time series with a linear trend. It does not provide a general definition of autocorrelation in time series. |
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