What term refers to random or unpredictable noise in a time series?

What term refers to random or unpredictable noise in a time series?

• A. Irregular variation
• B. Seasonal variation
• C. Secular trend
• D. R-squared

Answer: (A)

Random, unpredictable noise in a time series is captured by irregular variation. In a typical time-series decomposition, you separate the data into trend, seasonal, and irregular components. The irregular part represents fluctuations that can’t be explained by the underlying trend or the repeating seasonal pattern—these are the random shocks, measurement errors, and other unexplained moves. The other options don’t describe this noise: seasonal variation is the predictable pattern within each season; secular (trend) variation is the long-term direction; R-squared is a measure of how well a model fits the data, not a component of the time series itself.

Random, unpredictable noise in a time series is captured by irregular variation. In a typical time-series decomposition, you separate the data into trend, seasonal, and irregular components. The irregular part represents fluctuations that can’t be explained by the underlying trend or the repeating seasonal pattern—these are the random shocks, measurement errors, and other unexplained moves. The other options don’t describe this noise: seasonal variation is the predictable pattern within each season; secular (trend) variation is the long-term direction; R-squared is a measure of how well a model fits the data, not a component of the time series itself.