Which of the following statements correctly identifies the components of time series data ?

Explanation: Time series data typically has three main components: trend, seasonality, and irregular components (residuals) . The trend represents the long-term movement in data, such as a consistent increase in sales over years. Seasonality captures recurring patterns, like monthly sales peaks due to festivals. Irregular components, or residuals, represent random variations that cannot be explained by trend or seasonality. Understanding these components is vital for decomposing time series data, identifying underlying patterns, and building accurate forecasting models. Option A: Residuals are part of irregular components, but "noise" is not explicitly categorized as a separate time series component. Option C: Cyclic patterns are distinct from seasonality and are not a guaranteed component of time series. Option D: Moving averages are a forecasting method, not a fundamental component of time series. Option E: Autoregressive components belong to forecasting models like ARIMA, not core components of time series.