What is defined as "time-series data"?

Time-series data refers specifically to data points that are indexed or organized in chronological order. This type of data is critical for analyzing trends, patterns, and behaviors over time. When data is collected at regular intervals, it allows for the observation of changes and the prediction of future behavior based on historical trends.

For instance, stock prices recorded every minute throughout a trading day, or temperature readings collected hourly over a month, fall into this category, as they are all ordered by time. This time-based organization enables various analyses, such as forecasting, seasonal variation assessment, and change detection, which are essential in fields like finance, meteorology, and economics.

In contrast, the other options do not properly describe time-series data. Data collected from multiple sources may refer to a broader category of data aggregation, while geographical data pertains to spatial information. Data that requires frequent updating does not inherently relate to the sequencing of that data in time. Therefore, the distinguishing feature of time-series data is its chronological structure, making it unique in analytic approaches and methodologies.