2 How to plot stl() results?

One way to plot the stl() results is to use the plot() function, which produces a four-panel plot that shows the original time series, the seasonal component, the trend component, and the remainder component. However, this plot may not be very appealing or informative, especially if you want to compare different time series or highlight some features of the decomposition. A better alternative is to use the ggplot2 package, which offers more flexibility and customization options for creating visualizations.

3 How to use ggplot2 for stl() results?

To use ggplot2 for stl() results, you need to first convert the stl object into a data frame, using the as.data.frame() function. This will create a data frame with four columns: time, seasonal, trend, and remainder. Then, you can use the ggplot() function to create a plot object, and add layers of geoms, scales, and themes to customize your plot. For example, you can use geom_line() to draw lines for each component, scale_x_date() to format the x-axis as dates, and theme_bw() to use a black and white theme.

4 How to compare multiple stl() results?

If you want to compare multiple stl() results, for example, for different products or regions, you can use the facet_wrap() or facet_grid() functions from ggplot2. These functions allow you to create multiple plots with the same structure, but different subsets of data, based on a grouping variable. For example, if you have a data frame with stl() results for three products, you can use facet_wrap(~ product) to create three plots, one for each product, with the same axes and scales.

5 How to communicate stl() results?

To communicate your stl() results effectively, you need to consider your audience, your purpose, and your message. Your audience may be your colleagues, your managers, your clients, or your stakeholders. Your purpose may be to explain, to persuade, to recommend, or to report. Your message may be to highlight the main patterns, to identify the causes of fluctuations, to forecast future demand, or to suggest actions or strategies. Depending on these factors, you may need to adjust your plot design, your language, and your tone.

6 How to improve stl() results?

The stl() function is a powerful and flexible tool for seasonal and trend decomposition, but it is not perfect. It may not capture all the nuances and complexities of your demand data, such as outliers, cycles, or structural changes. It may also produce different results depending on the parameters you choose, such as the frequency, the smoothing, and the type of decomposition. Therefore, you should always check the quality and validity of your stl() results, using methods such as residual analysis, cross-validation, or comparison with other methods.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?