What type of analysis aims to identify patterns in historical data and project those patterns into the future?

Time series analysis is specifically designed to identify patterns within historical data collected over time and to forecast future values based on those established patterns. This type of analysis takes into account the sequential order of the data, making it ideal for analyzing trends, seasonal variations, and cyclical patterns. It uses past observations to project future outcomes, which is essential for forecasting in various fields such as economics, finance, and resource management.

In contrast, regression analysis focuses on the relationship between dependent and independent variables to model these relationships rather than strictly analyzing sequential data. Correlation analysis examines the strength and direction of relationships between two variables but does not inherently involve time-based analysis. Descriptive analysis summarizes data, providing insights and visualizations without predictive components. Thus, time series analysis stands out as the methodology aimed specifically at extending historical observations into future predictions.