Time series decomposition breaks a time series (Y) into four components: Trend (T) - the long-term progression of the series, indicating upward or downward movement over time. Cycle (C) - long-term oscillations or fluctuations not tied to seasons (e.g., economic cycles). Seasonal (S) - periodic fluctuations within specific intervals (e.g., monthly, quarterly). Irregular (I) - random, unpredictable influences or anomalies. In an additive model, the components are added: Y = T + C + S + I, and it's used when variations are roughly constant over time. In a multiplicative model, the components are multiplied: Y = T C S I, and it's more appropriate when fluctuations increase or decrease proportionally with the trend. Looking at the scatter plot, a strong exponential upward trend is observed, especially after 2016. The R value of 0.7202 indicates a strong fit of the trend line to the data. Given the current trajectory, one can predict that the store's annual sales will continue increasing sharply in the coming years, likely surpassing $6 million if growth remains exponential