Persistence in Time Series

In the last section, we explored the concept of autocorrelation. Autocorrelation in time and space can interfere with statistical analysis by causing the general assumption of the randomness and independence of measurements within a time series or sample to be violated. In this section, we will examine the implications of autocorrelation in time series data and how to account for autocorrelation when we cannot avoid it.

Our focus will be on temporal autocorrelation, but spatial autocorrelation is also an issue in cilmate data analysis.