Time series decomposition seeks
Time series decomposition seeks to separate the time series (Y) into 4 components: trend (T), cycle (C), seasonal (S), and irregular (I). What is the difference between these components?
The model can be additive or multiplicative. When do you use each?
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?
What predictions might you make about the store’s annual sales over the next few years?
Sample Answer
here is a brief explanation of the four components of time series decomposition:
- Trend (T): The trend component represents the long-term direction of the time series. It can be either increasing, decreasing, or constant.
- Cycle (C): The cycle component represents the short-term fluctuations in the time series. These fluctuations are typically caused by seasonal factors, such as the weather or holidays.
- Seasonal (S): The seasonal component represents the regular patterns that occur in the time series over a fixed period of time. For example, retail sales typically increase during the holiday season.
- Irregular (I): The irregular component represents the random fluctuations in the time series that cannot be explained by any of the other components.