From the course: Business Analytics: Forecasting with Seasonal Baseline Smoothing
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Seasonality in a baseline
From the course: Business Analytics: Forecasting with Seasonal Baseline Smoothing
Seasonality in a baseline
- [Instructor] Many baselines display what's usually termed "seasonality". That's a characteristic that causes the baseline to rise and fall at regular intervals. For example, increases in housing prices tend to be sharpest from the first to the second quarters, and to flatten during the other quarters of the year. Notice here that price increases every quarter but the increases are particularly sharp from the first to the second quarter of each year. Many cities and towns tend to collect property taxes twice a year. For example, during November and during May. Their tax receipts tend to spike during those two months, and to resume a fairly flat pattern the remainder of the year. The chart reflects that pattern with the spikes at the fifth month, May, and the 11th month, November. Take a look at this data on DWI Arrests over a one month period. Notice the two day spike in admissions each weekend during the charted period, with a return to a more horizontal pattern during the remainder…
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