Now, how well we built a nice regression model?

Did we really need it?

Could we have done something that easier?

Well, what if we used a Simple Moving Average model?

Or what if we used formulated it as in other regressive models saying,

let's use the new most recent weeks of observations and those are going to be our

predictors and we'll use those two data points to predict.

So if I look back one week, look back two weeks,

use that to predict what's demand going to be like in the current week.

It turns out, you're not going to do too poorly with that particular measure.

So our regression line for forecasting is in black, the purple line here giving us

that simple moving average based model, just using those two weeks of data.

And it looks reasonably similar, but the piece that I wanted to point out and

you can see it clearly in this case here as well that

the smoothing based approach where the auto-regressive model,

where we're relying just on those recent observations,

it tends to take longer to adjust than the regression-based model.

So any time there's that discontinuity, the end of a quarter, the end of a month.

Because I'm only looking back at the most recent two weeks,

it's not as quick to adjust as our regression-based model.

The other thing that we've gotta keep in mind is how far out into the future am I

trying to forecast?

Our regression includes a trend and month specific effect.

I can forecast a year out, no problem.

But with our simple moving average model,

what do I do if I want to go beyond a two-week period?

I actually need to keep on forecasting to produce those X variables, and

I'm actually going to have to resort to a simulation-based procedure to take into

account the amount of variation that I'm going to observe and

be able to forecast out that four-year period.