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