What type of data is typically stored in a time series database?

The correct choice highlights the primary function of a time series database, which is designed specifically for handling data points that are indexed by time. In this context, temporal data refers to any data that is collected or recorded over time, allowing for analysis of trends, patterns, and changes occurring over a specified period. Examples include stock prices, weather data, and sensor readings that track changes within a time frame.

Time series databases are optimized for this type of data, enabling efficient storage, retrieval, and analysis of large volumes of time-stamped entries. They often support functions that facilitate the aggregation, down-sampling, and irregular querying of time-related data, making them particularly useful in fields such as finance, IoT, and operations management where understanding temporal trends is essential.

In contrast, the other options refer to different types of data which are not appropriate for a time series database context. Static data refers to points that do not change over time and are typically not indexed by time; document-based data is structured around unstructured documents often stored in databases designed for retrieval and searching rather than for time-based queries; and key-value pairs, while they efficiently facilitate quick access to specific data, do not inherently incorporate a time aspect necessary for time series analysis.