JMP® 16 Updates in Time Series Platforms (2021-EU-45MP-745)

[#1]

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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Speaker	Transcript
	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.

[frankderuyck]

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.