What statistical function is used to identify patterns in data over specific periods?

The statistical function that identifies patterns in data over specific periods is time-series forecasting. This approach specifically analyzes data that are collected or recorded at different points in time. Time-series forecasting aims to understand underlying trends, seasonal variations, and cyclical patterns in the data, allowing for predictions about future values based on historical observations.

This technique is commonly used in various fields such as economics, finance, and environmental science, where data collected over time can reveal significant insights into trends or patterns that may be exploited for planning or decision-making. Time-series forecasting employs methods like moving averages, exponential smoothing, and ARIMA models to analyze the temporal structure of the data.

In contrast, regression analysis explores relationships between variables but does not focus specifically on time-based patterns. Standard deviation measures provide insights into the variability or dispersion of data rather than identifying trends over time. Hypothetical testing, typically associated with determining whether observed data falls within certain expectations, does not concern itself directly with time-based analysis. Thus, time-series forecasting is the most suitable choice for identifying patterns in data across specified periods.