Level: Intermediate
Peng Liu, JMP Principal Research Statistician Developer, SAS
Jian Cao, JMP Principal Systems Engineer, SAS
This talk will provide a comprehensive review of major updates in two time series-related platforms. More specifically, the updates include a forecasting performance-based model selection method, enhanced functions for studying the recently added state space smoothing models, and analysis capabilities using Box-Cox transformed time series. We will explain the motivations behind development efforts to help identify interesting use cases of the new features. We will present a few examples to illustrate some of the many possibilities for how these new features can be used. JMP 16 represents a major upgrade for time series platforms. Equipped with the new features, JMP opens the door to many intriguing new discoveries in time series analysis.
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This talk is to highlight some | |
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time series platforms. Three are | |
from time series platforms and | |
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do we need Box Cox | |
transformed time series? | |
Let's take a look at the data | |
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also known as a as an airline | |
passenger data set. | |
The original series is | |
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model from. | |
Why? Let's take a look at a plot... | |
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getting larger. And this series | |
cannot be handled by the | |
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in the second picture. So the | |
variation does not change | |
with the various times series ??? | |
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So in the literature people will | |
say, well, we will transform | |
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of the transform scale, in this case | |
here, it's the log scale. | |
Sending it to inverse | |
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transform. | |
In the past...in the past | |
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streamline the whole process. | |
What you need to do is to put | |
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need to do the models, make | |
forecast, then the software, | |
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will put log passengers into Y, | |
but now we don't have to. We | |
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to enter the Box Cox | |
transformation parameter Lambda. | |
Zero, it means it's a log | |
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the red triangle menu and click | |
either ARIMA or seasonal ARIMA. | |
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12 for seasonal part. | |
Without intercept. Click | |
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forecast taking care of the | |
inverse transformation. The | |
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will will show in this. | |
plot and the forecast had | |
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models is a workhorse in time | |
series forecast platform. | |
They can fit and forecast a lot | |
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performance is somehow comparable | |
to the forecasting | |
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study why it...why this type | |
of model works and why some some | |
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type model into the time series | |
platform which is designed to | |
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a function of the unknown | |
state, unobserved state. Here at | |
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variables and the error term | |
by either additive operations | |
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state is the level state time | |
series. Trend state forms a | |
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state, and also one of the | |
previous seasonal states. And | |
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previous trend state will | |
tremd to the next. | |
trend state and the level state | |
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point to another time point. And | |
there are more arrows...that there | |
are more states transitions than is | |
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series into Y and click OK. | |
To fit this type of model, we | |
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set, I'm going to enter 12 | |
for period. | |
And I'm going to click Select | |
Recommended button. | |
From the additive error models and | |
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this particular set, I'm going | |
to click constraint parameters | |
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recommended models to fit these | |
type of...these time series and | |
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model with smaller AIC and | |
my eyes are on the first two | |
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models. And let me | |
overlay the forecast | |
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from the original time series | |
more nicely. | |
So in my preference, I would | |
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difference? Let me open the | |
first one MAA...MAM. | |
Let's go down below. This | |
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this one | component states. |
This is a special for this | |
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the first letter. | |
And the trend is additive by | |
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second part of this report are | |
the...are the state component | |
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part is the prediction of | |
this specific state. | |
The period of the time series | |
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has an increasing pattern in | |
the past. It keeps increasing | |
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series and the pattern continues | |
toward the future, and this | |
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observed, but the forecast is | |
flat. This bothered me. | |
Now let's look at second | |
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state component graph. Level is | |
increasing in the past had | |
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future. This is more reasonable | |
plot that I can accept. So is it | |
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on to the second slide. | |
This slide and then the next | |
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on interpreting the forecasts | |
from from this model...this type of | |
models. Here I would like | |
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up. I listed half of them here. | |
Oh, nearly half. So let's focus | |
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some increase trend and will | |
taper off towards the end. | |
And on the other hand, we can | |
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see from the forecast using this | |
type model. If seasonality | |
is not involved. When I | |
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the first one, this is | |
a flat forecast if the seasonality | |
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have a linear increase | |
patern and so on so forth | |
similar to the others. Now | |
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it's merely increasing. | |
After applying the | |
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the multiplicative seasonality on | |
the top of our increasing | |
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this type...different type of | |
shapes | flat |
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we get those different...different | |
shapes. So I I re entering ??? | |
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what we eventually see in | |
the forecast. | |
You have the flat patterns or | |
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parameters. So I separated | |
these parts and also I | |
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trend will usually look flat, | |
we will get an increase | |
pattern in the level state. | |
When it's linear and | |
when it's curved. | |
It's all depends on how this | |
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increasing or decreasing in the | |
level exponentially. So this is | |
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is lean and think of it as | |
compound interest rate if | |
if the level state increase | |
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they make forecasts, they try to... | |
try not to overshoot | |
or undershoot the forecast that | |
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how to interpret | |
the forecast from state | |
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second one, none of of these | |
models are stationary. They are | |
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So if you are considering these | |
time series. Things | |
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third one, if you just see | |
that time series not | |
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a result in a...in | |
the next slide that will fit | |
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compare across type of | |
model be careful. | |
This slide is to show how... | |
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is the forecast. | |
And similarly, I plot my | |
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apply these type of state space | |
smoothing models to stationary | |
time series? Here I simulate a | |
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models to this time series, the | |
best model turns out to be in an | |
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rather different becauses it is | |
a random walk model and the | |
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feature in this presentation | |
forecast on holdback. | |
This feature allows you | |
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one is from another model. | |
And then you can compare these | |
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to activate this feature. Then I | |
need to specify | |
the length of the holdback | |
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click Select Recommended, | |
and check Constraint | |
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portion of the series, | |
we listed the holdback | |
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by default, but you can | |
always change the metrics you | |
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reports are similar to | |
to that got from the analysis | |
results without activating this | |
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let's let's let me summarize | |
what we have learned from | |
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performance over the holdback | |
data. But those criterias are | |
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process. We see the rather | |
different from how we use | |
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part of the model fitting | |
process, so this is something | |
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holdback to evaluate | |
different models based on their | |
forecasting performance. So we | |
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column is that time series | |
indicator. Y is time series | |
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summarize the data set, either | |
time or time series, by | |
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specification or change the | |
model selection strategy, we | |
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check...change selection in the | |
first combo box to forecasting | |
performance. Then we can choose | |
forecasting performance | |
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we want forecast. But you can | |
choose any...change to any | |
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using the training | |
time series, select the best | |
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series platform. First analyze | |
Box Cox Transformed time series. | |
The second one is fit state | |
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as well and using it as | |
And model selection method. | |
Thank you very much. |
Very nice, useful presentation, well explained; user friendly method, a contender for ARIMA !
I have some time series for industrial processes and will give this a try. I now have jpm pro 15 but I need pro 16 right?
@frankderuyck Sorry for the delayed response . To take the full benefits you'd need JMP 16. However, most of the new Time Series Forecast features are available in JMP 15. Both Time Series and Time Series Forecast platforms are regular JMP.