14 Descriptive Analysis
Time Series Data is
- Numerical data ordered over time
- The time intervals can be annually, quarterly, daily, hourly, etc.
- The sequence of the observations is important
Example:
| Year | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 |
|---|---|---|---|---|---|---|---|
| Sales | 75.3 | 74.2 | 78.5 | 79.7 | 80.2 | 82.3 | 85.8 |
A time-series plot is a two-dimensional plot of time series data:
- the vertical axis measures the variable of interest
- the horizontal axis corresponds to the time periods
Some important questions to first consider when first looking at a time series are:
- Is there a trend, meaning that, on average, the measurements tend to increase (or decrease) over time?
- Is there seasonality, meaning that there is a regularly repeating pattern of highs and lows related to calendar time such as seasons, quarters, months, days of the week, and so on?
- Are there outliers? In regression, outliers are far away from your line. With time series data, your outliers are far away from your other data.
- Is there a long-run cycle or period unrelated to seasonality factors?
- Is there constant variance over time, or is the variance non-constant?
- Are there any abrupt changes to either the level of the series or the variance?
A time series has three components:
- Trend: long-term direction
- Seasonality: periodic behavior
- Cyclical:
- Residual: irregular fluctuations