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Seasonality Monthly v2.0-Sanjay
Monthly seasonality refers to recurring patterns or fluctuations in data that repeat every month due to predictable factors like weather, holidays, or business cycles. For example, retail sales often peak in December due to holiday shopping, while utility usage may rise in summer because of air conditioning. Identifying monthly seasonality helps businesses forecast demand, allocate resources, and plan operations more accurately.
Seasonality significantly impacts forecasting because it introduces predictable, recurring patterns into data that must be accounted for to improve accuracy. If seasonal effects—such as higher sales during holidays or increased energy demand in summer—are ignored, forecasts can be misleading, leading to overproduction or stockouts. Incorporating seasonality into models (e.g., using seasonal decomposition or SARIMA) helps capture these cyclical variations, ensuring that predictions reflect real-world trends rather than just overall averages.
Seasonality significantly impacts forecasting because it introduces predictable, recurring patterns into data that must be accounted for to improve accuracy. If seasonal effects—such as higher sales during holidays or increased energy demand in summer—are ignored, forecasts can be misleading, leading to overproduction or stockouts. Incorporating seasonality into models (e.g., using seasonal decomposition or SARIMA) helps capture these cyclical variations, ensuring that predictions reflect real-world trends rather than just overall averages.
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