What is a moving average derived from successive segments of a series known as?

A moving average derived from successive segments of a series is indeed referred to as a moving average. This statistical technique smooths out fluctuations in data by creating averages over a specified number of periods, which helps identify trends over time. By calculating averages that move along with the data series, this method enables analysts to track changes more effectively without the distortions that may arise from short-term variations.

The other options, while they relate to averages, do not capture the specific nature of a moving average. A simple average calculates the overall average of a dataset without accounting for changes over time, while a cumulative average aggregates all past data, rather than segmenting it into moving averages. A weighted average assigns different weights to data points based on their importance, which differs fundamentally from the straightforward calculation of moving averages that treats all included data points equally within each segment. Understanding these distinctions is crucial for analyzing time-series data efficiently.