Solution

Seasonality in time series data represents recurring patterns that follow a consistent interval, such as daily, weekly, monthly, or annually. It’s driven by factors like weather, holidays, or other cyclical influences specific to the data’s context. In sales data, for example, seasonality may reflect increased sales during holidays. Recognizing seasonality is critical for accurate forecasting, as it allows analysts to adjust for these predictable fluctuations. Option A (Trend) is incorrect because the trend represents the long-term progression, not repetitive patterns. Option B (Noise) is incorrect as noise refers to random, non-systematic fluctuations without pattern. Option C (Residuals) is incorrect because residuals are the irregular components left after accounting for trend and seasonality. Option E (Irregular Component) is incorrect because irregular components are unpredictable fluctuations without cyclical patterns.