Understanding Seasonality in Time-Series Data

In time-series analysis, seasonality refers to periodic fluctuations that occur at regular intervals within a time series. These fluctuations can result from various factors such as weather, holidays, or economic cycles. For example, retail sales typically increase during the holiday season each year.

Consider the following time series data which represents monthly sales for a retail store over the past two years:

• January: 100
• February: 120
• March: 130
• April: 150
• May: 200
• June: 250
• July: 300
• August: 280
• September: 240
• October: 230
• November: 250
• December: 300

Which of the following statements correctly describes a characteristic of seasonality in this time series data?