Purpose

MAPE (4.19%): Excellent in-sample accuracy, indicating the model explains historical revenue patterns with minimal percentage error
Ljung-Box p-value (0.924): Residuals exhibit white noise properties, confirming the model adequately captures temporal dependencies
Model Specification: ARIMA(0,0,0)(2,1,0)[12] with seasonal differencing, suggesting strong 12-month seasonality requiring first-order differencing to achieve stationarity
Forecast Range: Predictions span $13,048\u2013$16,740 across 2024, with 80% confidence intervals averaging \u00b1$1,000 around point estimates

STL decomposition isolates the three fundamental drivers of Acme Corp's monthly revenue: long-term trend direction, predictable seasonal cycles (12-month pattern), and irregular noise. This separation is critical for understanding whether forecast accuracy stems from capturing genuine business patterns or merely fitting historical volatility, directly supporting the budget planning objective.

Key Findings

Seasonal Frequency: 12-month cycle identified, indicating strong recurring annual patterns in revenue
Component Range: Observed values span $8,965–$15,767, with remainder (noise) ranging from –$1,818 to positive values, showing moderate unexplained variation
Remainder Characteristics: Mean near zero (–$64) with standard deviation of $715, suggesting residual noise is relatively small compared to total revenue scale
Data Completeness: 72 observations across 6 years provide sufficient history for reliable seasonal pattern extraction

Interpretation

The decomposition reveals that Acme Corp's revenue is primarily driven by a consistent 12-month seasonal pattern overlaid on a trend component. The relatively small remainder component (±$715 around a $12,304 mean) indicates that trend and seasonality together explain most revenue variation, validating the ARIMA model's ability to capture predictable patterns. This structure supports the 4.19% MAPE accuracy—the model effectively lever