Identifying Seasonal Sales Trends

Easy Time-series Analysis Seasonality

In the context of time-series analysis, seasonality refers to the periodic fluctuations in a time series that occur at regular intervals due to seasonal factors. For instance, retail sales often see increases during the holiday season each year. Understanding and correctly identifying seasonality is crucial for making accurate forecasts and decisions.

Suppose you have monthly sales data for a retail store over five years. After applying seasonal decomposition methods, you observe the following seasonal component values for the months:

January: -5, February: 0, March: 5, April: 10, May: 15, June: 20, July: 15, August: 10, September: 5, October: 0, November: -5, December: -10.

Based on this seasonal pattern, what trend can be concluded about the store's sales during the year?

Sales are consistently increasing throughout the year.

Sales peak in May and June, suggesting a strong mid-year sales period.

Sales are highest in the winter months and lowest in late summer.

Hint

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