Time series decomposition seeks to separate the time series AND into components: trend T cycle C seasonal S and irregular I What is the difference between these components?

Trend T: A trend represents a longterm movement or direction of a time series. It captures the general pattern of growth or decline in the data. This process can be up down, or stationary no up or down movement

Cycle C: The cycle component refers to the semiperiodic changes or oscillations of the periodic system. It represents a recurring pattern but not necessarily in time and nature. Cycles are usually longer than seasonal cycles and can extend over several years.

Seasonal S: This part of the season presents a series that repeats itself during a specific period in a year. It represents structural and predictable changes that occur over a period, months, weeks, days, or even hours. Seasonal patterns occur due to factors such as weather, holidays, or cultural events.

Irregularity I: This irregular component, also known as residual or noise, represents the variability and unpredictability in the time series. It includes any or unexpected change that cannot be explained in behavior, any system, or timer. Those are often associated with nonexistent, indicating well and zoom.

The model can be additive or multiplicative. When do you use each?

The choice of an additive or multiplicative model depends on the balance of the amount of change over time and the magnitude of the process. In the additive model, the time variable is assumed to be dependent on the system, while in the multivariate model, the time variable is assumed to be proportional to the system.

Review the scatter plot of the exponential trend of the time series data. Do you observe a trend? If so what type of trend do you observe?

Based on the scatter plot of the exponential trend of the time series data, we can observe a clear upward trend. This suggests that the store\'s sales are increasing over time.

What predictions might you make about the stores annual sales over the next few years?

Based on the characteristics of the spreading feature of the time series data, we can see a clear upward trend. We can predict the annual sales of the store over the next few years by reporting these trends, but it is important to note that external factors can affect store sales and should be considered when making policybased decisions.