[MUSIC PLAYING]
MIKE GILLIAND: Hi, this is Mike, from SAS.
And if you love business forecasting the way I do,
you know it's a job for professionals, requiring
enterprise-grade software that provides large scale automation
of the forecasting process.
And that's what you get with SAS Visual Forecasting.
Let's take a look.
SAS Visual Forecasting provides a flexible user interface
built around the notion of forecasting projects
and forecasting pipelines.
Here, I've set up a project for a consumer products company
using the default auto-forecasting template.
Auto-forecasting provides automatic model building
with exponential smoothing in ARIMAX models.
Because I'm a big fan of naive models,
I've added a naive modeling node to this.
Naive models are useful as a point of comparison
to the more sophisticated models that you use.
They're easy to compute.
And provide this good baseline of performance comparison.
So the pipeline consists of a set
of nodes that are executed sequentially
as part of the project.
The first is this data node.
And we'll take a look in here because it provides
some additional useful information for visualizing
the data in your time series.
Here, we've got 1,337 overall time series.
We see from this diagram, the overall average
of the 1,337 one in two standard deviations around,
and then the full range of the time series here.
You can also click down to individual products--
sold through all the distribution centers.
Or go down to SKU level.
For example, the individual product at a distribution
center.
Going into these nodes for the modeling,
you can find some characteristics
about the models that were selected.
And then you can go down to the Model Comparison node
and take a look at the results of executing this pipeline.
And what we see is that auto-forecasting
was selected as the champion model
based on our selection criteria of the weighted MAPE.
It beat the naive modeling, by not a huge amount.
But that's not surprising.
In many instances, naive models can do well
on the types of demand patterns you're dealing with.
Artificial intelligence and machine learning
are hot areas of research right now, both for academics
and for industry data scientists,
trying to find the best way to utilize machine learning
methods in forecasting.
With SAS Visual Data Mining and Machine Learning,
you can get just about all the machine learning
types of models that you can dream of.
With SAS Visual Forecasting, we have
three common neural network models
built right into the interface.
Let's take a look at how those work.
Multi-stage panel series neural network and also
stacked model that uses neural networks with some time series.
Now in this pipeline, we've run these
and do the model comparison.
Take a look.
And it so happens that this stacked model
provided the best forecast based on a weighted MAPE criterion.
Another useful thing to do in forecasting
is to segment your data before you
start generating your models.
Here, I've set it up using the segmentation template.
You see some different nodes here,
first being a demand classification profile.
So let's take a look at what that does.
The demand classification will automatically
segment your time series into 11 possible categories based
on the behavior, the characteristics of those demand
patterns.
In this case, we see 25 retired series.
These were found to have zeros at the end of their history.
They looked like they had been retired
so you don't have to worry about forecasting them anymore.
There were 673 items that had year-round demand that was
non-seasonal, 636 that had year-round demand that was
seasonal, and three that had demand only during in-season--
for example, something like a snow shovel.
Where you would sell it during the fall and winter,
you would close out sales to get rid of your inventory
in the spring.
And have no sales at all over the summer.
Running the segmentation pipeline,
the demand classification modeling
will generate a specific model for each
of these types of each of these segments that we've created.
We merge these segments back together, go down to the output
where we can then feed these forecasts
to downstream planning systems.
Also on the output node, we can take a traditional look
at forecasting.
Here, you see all the historical data points,
the model that was created.
And we also see the future forecasts
with the prediction interval that
gives you some idea of our certainty in that forecast.
Down below, we get a tabular view of the same data in a row
where we can put in manual overrides, if we wish to.
On the left, we can navigate through the hierarchy, picking
out specific products or locations
in which to review the data or make our overrides.
So that has been a quick view of SAS Visual Forecasting.
I hope you enjoyed it.
Thanks for watching.