My colleague Jessica Curtis is a Solutions Architect at SAS, specializing in forecasting. In this two part contribution, Jessica tells us what time series segmentation is, why we use it, and how it adds value to your forecasting process.
Guest Blogger Jessica Curtis on Time Series Segmentation
The large-scale forecasting challenge affects organizations across many different industries.
Whether you’re a retailer forecasting sales to determine the right assortment of SKUs in your stores, a hotel forecasting the demand for room occupancy, or a wholesaler forecasting the right level of inventory to keep your customers in stock. You could be a media company, forecasting power consumption in your data centers; an airline, forecasting passenger and freight traffic; or a telecom company, forecasting call center demand for labor capacity planning.
With any large-scale forecasting challenge, the most important first step is to understand the structure of your time series data.
The Ideal Time Series
In a perfect world, all your time series would look something like this: the data is high volume, with a long history, a steady, repeatable, and predictable pattern, with very few (if any) missing values.
In this ideal forecasting world, good forecasting results are easily achieved through an automated forecasting engine and one forecast modeling strategy. Unfortunately, real world time series data is not always so clean.
The Real Time Series
In reality, there is much more diversity in the patterns of our time series data.
There are stable time series, such as laundry detergent sales, and seasonal time series, such as the demand for grills. There could be time series with a level shift in demand; for example, when a market expands to new regions or sales channels. There are also situations where there is limited history in the time series, which would be brand new items incorporated into your offering. There are time series with sparsity of data or intermittent demand, such as spare parts for cars. And there could also be items that only sell at certain times of the year; for example, Halloween costumes.
Given the range of time series patterns, one modeling strategy for all these different types of time series will not give you the best forecast. So now the question becomes: how do I manage all the different patterns of my time series data?
Time Series Segmentation
The answer is time series segmentation. Time series segmentation is the method of dividing time series data into distinct types or segments based on the underlying properties of the time series. It is the one of the most important first steps in the forecasting process. What this allows you to do is then apply a customized forecast modeling strategy to each time series segment.
We'll continue this discussion tomorrow...
Time Series Segmentation Recorded Webinar
If you are eager to see how the story ends, watch the 15-minute webinar Time Series Segmentation by Jessica, which includes a 6-minute demonstration of the new SAS Forecast Server Client by SAS forecasting product manager Laura Ryan.