cancel
Showing results for 
Show  only  | Search instead for 
Did you mean: 
JMP® 16 Updates in Time Series Platforms (2021-EU-45MP-745)

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.

 

 

Auto-generated transcript...

 

Speaker

Transcript

This talk is to highlight some
00 11.600
3
time series platforms. Three are
from time series platforms and
00 34.700
38.400
10
11
12
do we need Box Cox
transformed time series?
Let's take a look at the data
00 00.933
06.200
18
also known as a as an airline
passenger data set.
The original series is
00 23.900
23
model from.
Why? Let's take a look at a plot...
00 43.433
27
getting larger. And this series
cannot be handled by the
00 01.666
31
in the second picture. So the
variation does not change
with the various times series ???
00 20.266
36
So in the literature people will
say, well, we will transform
00 33.800
38.700
41
42
43
of the transform scale, in this case
here, it's the log scale.
Sending it to inverse
00 04.366
49
transform.
In the past...in the past
00 21.966
53
streamline the whole process.
What you need to do is to put
00 39.133
57
need to do the models, make
forecast, then the software,
00 55.933
61
will put log passengers into Y,
but now we don't have to. We
00 15.033
66
to enter the Box Cox
transformation parameter Lambda.
Zero, it means it's a log
00 28.333
31.166
37.433
73
the red triangle menu and click
either ARIMA or seasonal ARIMA.
00 01.766
78
12 for seasonal part.
Without intercept. Click
00 19.633
22.933
24.500
28.733
85
86
forecast taking care of the
inverse transformation. The
00 49.600
91
will will show in this.
plot and the forecast had
00 05.033
08.800
11.100
17.866
98
models is a workhorse in time
series forecast platform.
They can fit and forecast a lot
00 35.733
39.633
104
performance is somehow comparable
to the forecasting
00 59.366
110
111
study why it...why this type
of model works and why some some
00 18.600
115
116
type model into the time series
platform which is designed to
00 43.900
120
a function of the unknown
state, unobserved state. Here at
00 01.000
124
variables and the error term
by either additive operations
00 16.266
19.200
129
130
state is the level state time
series. Trend state forms a
00 40.733
134
135
state, and also one of the
previous seasonal states. And
00 59.733
03.133
140
141
previous trend state will
tremd to the next.
trend state and the level state
00 25.966
146
point to another time point. And
there are more arrows...that there
are more states transitions than is
00 49.700
152
series into Y and click OK.
To fit this type of model, we
00 12.300
16.766
160
161
set, I'm going to enter 12
for period.
And I'm going to click Select
Recommended button.
From the additive error models and
00 47.666
169
this particular set, I'm going
to click constraint parameters
00 05.966
173
recommended models to fit these
type of...these time series and
00 23.666
27.966
179
180
model with smaller AIC and
my eyes are on the first two
00 52.300
184
models. And let me
overlay the forecast
00 09.366
10.566
17.666
190
191
192
193
from the original time series
more nicely.
So in my preference, I would
00 45.133
198
difference? Let me open the
first one MAA...MAM.
Let's go down below. This
00 05.533
10.666
204
this one component states.
This is a special for this
00 32.900
209
the first letter.
And the trend is additive by
00 50.233
213
second part of this report are
the...are the state component
00 06.100
217
part is the prediction of
this specific state.
The period of the time series
00 27.500
222
223
has an increasing pattern in
the past. It keeps increasing
00 48.266
227
series and the pattern continues
toward the future, and this
00 03.466
231
observed, but the forecast is
flat. This bothered me.
Now let's look at second
00 17.333
20.366
23.566
27.933
239
240
state component graph. Level is
increasing in the past had
00 44.366
48.466
245
246
247
248
future. This is more reasonable
plot that I can accept. So is it
00 14.866
252
on to the second slide.
This slide and then the next
00 45.100
259
on interpreting the forecasts
from from this model...this type of
models. Here I would like
00 01.933
07.233
265
up. I listed half of them here.
Oh, nearly half. So let's focus
00 27.033
30.800
271
some increase trend and will
taper off towards the end.
And on the other hand, we can
00 54.233
277
see from the forecast using this
type model. If seasonality
is not involved. When I
00 11.066
14.200
19.566
284
285
286
the first one, this is
a flat forecast if the seasonality
00 46.833
51.266
291
have a linear increase
patern and so on so forth
similar to the others. Now
00 06.800
12.566
297
it's merely increasing.
After applying the
00 34.033
302
the multiplicative seasonality on
the top of our increasing
00 48.966
306
this type...different type of
shapes flat
00 05.666
10.733
311
312
we get those different...different
shapes. So I I re entering ???
00 33.466
40.433
318
what we eventually see in
the forecast.
You have the flat patterns or
00 00.733
323
parameters. So I separated
these parts and also I
00 14.866
20.400
328
329
330
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
00 52.233
57.666
339
increasing or decreasing in the
level exponentially. So this is
00 13.466
343
is lean and think of it as
compound interest rate if
if the level state increase
00 32.466
348
they make forecasts, they try to...
try not to overshoot
or undershoot the forecast that
00 59.833
355
how to interpret
the forecast from state
00 20.133
359
second one, none of of these
models are stationary. They are
00 37.466
364
So if you are considering these
time series. Things
00 55.566
59.600
369
third one, if you just see
that time series not
00 12.000
373
374
a result in a...in
the next slide that will fit
00 31.933
378
compare across type of
model be careful.
This slide is to show how...
00 51.800
383
is the forecast.
And similarly, I plot my
00 07.400
387
apply these type of state space
smoothing models to stationary
time series? Here I simulate a
00 32.633
393
394
395
models to this time series, the
best model turns out to be in an
00 54.266
399
400
rather different becauses it is
a random walk model and the
00 13.466
16.466
18.733
22.333
408
feature in this presentation
forecast on holdback.
This feature allows you
00 37.966
40.500
45.833
415
416
417
418
one is from another model.
And then you can compare these
00 11.833
423
424
to activate this feature. Then I
need to specify
the length of the holdback
00 35.200
38.466
430
431
432
click Select Recommended,
and check Constraint
00 06.433
438
439
portion of the series,
we listed the holdback
00 26.000
443
444
by default, but you can
always change the metrics you
00 39.900
44.866
449
reports are similar to
to that got from the analysis
results without activating this
00 05.433
454
455
let's let's let me summarize
what we have learned from
00 25.566
459
performance over the holdback
data. But those criterias are
00 41.366
463
process. We see the rather
different from how we use
00 57.700
467
part of the model fitting
process, so this is something
00 17.000
471
holdback to evaluate
different models based on their
forecasting performance. So we
00 32.033
35.366
478
column is that time series
indicator. Y is time series
00 55.666
482
summarize the data set, either
time or time series, by
00 09.533
12.066
16.600
488
489
specification or change the
model selection strategy, we
00 39.200
494
check...change selection in the
first combo box to forecasting
performance. Then we can choose
forecasting performance
00 04.633
500
we want forecast. But you can
choose any...change to any
00 17.833
25.466
30.866
506
507
using the training
time series, select the best
00 49.800
52.333
54.766
57.900
00.966
515
series platform. First analyze
Box Cox Transformed time series.
The second one is fit state
00 27.666
521
522
as well and using it as
And model selection method.
Thank you very much.

 

Comments

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?

jiancao

@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.