Financial Risk Manager Part 1

Seasonality in sales data refers to the presence of variations that occur at specific regular intervals less than a year, such as weekly, monthly, or quarterly. Seasonality may be caused by various factors, such as weather, holidays, and the like. In the context of time series analysis, if there is significant seasonality in the data, the autocorrelation plot should show spikes at lags equal to the period. For instance, for monthly data, a seasonality effect would yield significant peaks at lag 12, 24, 36, 48, and so forth. This is because the sales data is repeating a specific pattern every specific period (e.g., every 12 months for yearly seasonality). Therefore, the autocorrelation will be high at these lags, indicating a strong relationship between the sales data points 12, 24, 36, etc. months apart.