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A retail store manager uses time series models to understand shopping trends. Time series models are particularly useful to track variables such as revenues, costs, and profits over time. Time series models help evaluate performance and make predictions.
Review the scatter plot of the store’s sales from 2010 through 2021 to answer the questions. You may also review the annual sales data and chart in Excel, if desired.
Respond to the following:
- 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?
Chart
Sales
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 260123 256853 274366 290525 322318 380921 541925 909050 1817521 3206564 4921005 5686338
Data
Fiscal Year | Sales |
2010 | 260,123 |
2011 | 256,853 |
2012 | 274,366 |
2013 | 290,525 |
2014 | 322,318 |
2015 | 380,921 |
2016 | 541,925 |
2017 | 909,050 |
2018 | 1,817,521 |
2019 | 3,206,564 |
2020 | 4,921,005 |
2021 | 5,686,338 |
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