Time-weighted averages are a way to calculate an unbiased average when working with irregularly sampled data, which is common in time-series data analysis. The problem arises because more frequent sampling points can skew the average if not accounted for, especially when dealing with applications like industrial IoT, remote sensing, and trigger-based systems. To solve this, TimescaleDB introduces hyperfunctions, new SQL functions that simplify working with time-series data. One of these hyperfunctions enables computing time-weighted averages quickly and efficiently, making it possible to analyze time-series data more effectively and gain productivity boosts for projects. By using the `time_weight` hyperfunction, developers can calculate time-weighted averages in a concise and efficient way, enabling them to build complex analyses and identify patterns in their data.