What type of data is typically analyzed using time series analysis?

Time series analysis focuses on data points collected or recorded at specific time intervals, allowing for the examination of trends, seasonal patterns, and cyclical movements over time. This type of analysis primarily utilizes interval and ratio data, as these data types allow for meaningful mathematical operations and comparisons over time.

Interval data has a defined scale with consistent intervals between values, while ratio data includes all the characteristics of interval data, along with a true zero point, making it possible to express a wide range of data attributes, such as growth rates or time duration. Such characteristics are essential in time series analysis, where the primary goal is to track changes in data points across a timeline accurately.

In contrast, qualitative data, cross-sectional data, and nominal data do not lend themselves as effectively to this type of analysis. Qualitative data focuses on descriptive characteristics rather than measurable increments. Cross-sectional data captures a single point in time rather than observing changes over time. Nominal data categorizes variables without a quantitative aspect, which is not suitable for time series analysis that seeks to analyze numerical values across temporal dimensions. Thus, interval and ratio data stand out as the most appropriate types for conducting time series analysis.