Forecasting catalog sales
A catalog company is interested in forecasting monthly sales of its men's clothing line, based on 10 years of their sales data.
This example uses the flow Forecasting Catalog Sales, available in the example project you imported previously. The data file is catalog_seasfac.csv.
We've seen in an earlier tutorial how you can let the Expert Modeler decide which is the most appropriate model for your time series. Now it's time to take a closer look at the two methods that are available when choosing a model yourself—exponential smoothing and ARIMA.
To help you decide on an appropriate model, it's a good idea to plot the time series first.
Visual inspection of a time series can often be a powerful guide in helping you choose. In
particular, you need to ask yourself:
- Does the series have an overall trend? If so, does the trend appear constant or does it appear to be dying out with time?
- Does the series show seasonality? If so, do the seasonal fluctuations seem to grow with time or do they appear constant over successive periods?