Statistical forecasting models can be roughly divided into two types: Time series forecasting, the most common one, and predictive models. This division can be a bit artificial at times—you could use both approaches in a single model—but let’s keep it for now because it will simplify our understanding of the topic.
14.1 Time series forecasting
Time series models look at historical demand patterns (trend and seasonality) and extrapolate them into the future. To do so, they usually decompose demand into three subcomponents: level, trend, and seasonality. Most time series forecasting models are also called univariate as they only use one variable (historical demand) to predict an outcome (future demand).
- Level: The level is the average value around which the demand varies over time. As you can observe in figure 14.1, the level often looks like a smoothed version of the demand. Example: “On average, we sell ten books per day.” Figure 14.1 illustrates the sales level of Toyota in Norway between 2007 and 2017.
Figure 14.1 Historical sales and level of Toyota cars in Norway42
