Education and Career Forum

Explain the method of seasonal variation

The seasonal variation method refers to analyzing how some variable changes over the course of a year due to seasonal effects. This is a useful technique for:

• Identifying seasonal patterns and cycles. Looking for recurring increases, decreases, peaks and troughs that happen at the same times every year can reveal seasonal trends. Things like summer vacations, back-to-school seasons, holidays, planting/harvesting cycles, etc. often create seasonal patterns.
• Adjusting for seasonal influences. If a variable is affected by seasons, the seasonal variation can be calculated and removed to get a "seasonally adjusted" value. This allows for more accurate comparisons over time. For example, retail sales data is often seasonally adjusted.
• Forecasting seasonal impacts. Past seasonal patterns can be used to predict how a variable might change over the next year based on the seasons. For example, anticipating sales increases in the Christmas shopping season.
• Explaining fluctuations and anomalies. Deviations from normal seasonal patterns can sometimes be explained by analyzing seasonal impacts. For instance, an unusually hot summer could impact air conditioning sales or cold weather apparel.

Some key steps in the seasonal variation method include:

• Collecting data on the variable of interest over multiple years. At least 3-5 years of monthly or quarterly data is best.
• Calculating the seasonal index or average for each period (month, quarter). The seasonal index represents the typical seasonal change or percentage impact.
• Subtracting the seasonal index from each period's raw value to get the seasonally adjusted value. This removes the normal seasonal increase/decrease.
• Analyzing the adjusted values to identify any non-seasonal trends, shifts or anomalies over time. Seasonally adjusting the data allows these longer-term changes to emerge more clearly.
• Forecasting future values by applying the normal seasonal indices to seasonally adjusted data or recent raw values. The forecast accounts for typical seasonal changes but reflects the overall level of the adjusted trend.
• Closely monitoring the actual values over time and adjusting the method or seasonal indices as needed to improve future forecasts. The seasonal impacts can change over longer periods of time.