ARIMA model missing seasonality?

Just getting started with Alteryx ARIMA modelling and set up the below workflow which is generating weird results -- the time series compare picture shows the ARIMA and ETS forecasts which are clearly off, even though the decomposition plot seems to pick up the seasonality in the data points -- at the very least I would have expected the ARIMA plot to follow the seasonality just with large error areas to account for the remainder noise. Any thoughts on what's going on here?

Predictive Analysis

Accepted answers

HossC

Hey @Alternewbie,

To answer your first question, the ARIMA tool in Alteryx uses the R function auto.arima() when choosing the the order of AR, the order of MA, and the degree of differencing.

Per the documentation for this function: "The number of seasonal differences is sometimes poorly chosen. If your data shows strong seasonality, try setting D=1 rather than relying on the automatic selection of D." auto.arima() documentation

This is actually what you did in the "ARIMA1" model when you set the level of seasonal differencing = 1!

So, when we look at the "ARIMAAuto" model, which uses default settings, the tool chooses an ARIMA(1,0,2)(0,0,0,) model. The fact that there is no differencing doesn't bode well when modeling data that has the amount of seasonality in your data, which is why the "ARIMAAuto" model performs poorly.

However, when we look at the "ARIMA1" model, where you set the level of differencing to 1, we see that it lands on an ARIMA(0,1,1)(0,1,0) model. Forcing one level of differencing allows the "ARIMA1" model to account for seasonality and produces much better results.

A helpful overview of AIMA models and the auto.arima() can be found here: Forecasting: principles and practice - 8 ARIMA models

To answer your second question, at the moment, that feature is not present in the model factory. So if you want to force differencing to multiple variables, you will need to bring in an ARIMA tool for each one. This seems like it would be a useful feature, as it was mentioned here as well, so feel free to request it here.

Thanks!

Hoss Carroll

Customer Support Engineer

Alteryx

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TonyM

Hi @Alternewbie,

From the TS Compare it does look like something is off in the model or perhaps the configuration of the tools. Would you be able to provide the workflow and data? It is tough to determine the PDQ values in the ARIMA model without looking at the ACF plot.

Thanks,

Alternewbie

Sure -- workflow is attached. I kept digging in on ARIMA modelling after posting that question and added a second ARIMA model -- this time I manually selected Model Customization > Customize the parameters used for automatic model creation > The seasonal components > Alter the degree of seasonal differencing and I at least get a forecast out of the model (the manually adjusted is ARIMA1 and the base is ARIMAAuto in the attached picture), which is nice!

So my question is now a 2 parter:

1. Now that you can see the data in my workflow, any thoughts on what's happening with my ARIMA auto?

2. If there's just an issue with the data and an auto ARIMA can't divine the seasonality, is there any way to manually alter the degree of seasonal differencing in Model Factory? I want to apply this process to multiple units at once but most of what I've read indicates that the Model Factory uses auto ARIMA which isn't helpful given my issue, so being able to adjust the seasonal differencing to at least get a forecast seems like it could be useful

AlteryxARIMAExpample.yxmd

Untitled.jpg

MikeSp

Hello @Alternewbie,

I'm afraid the Excel file didn't make it on the board. Would you be able to export your workflow with the Excel file as an included asset or provide the Excel file?

Thanks!

Alternewbie

Whoops -- sorry about that -- excel attached.

SimpleSales_11.02.17.xlsx

HossC

Hey @Alternewbie,

To answer your first question, the ARIMA tool in Alteryx uses the R function auto.arima() when choosing the the order of AR, the order of MA, and the degree of differencing.

This is actually what you did in the "ARIMA1" model when you set the level of seasonal differencing = 1!

A helpful overview of AIMA models and the auto.arima() can be found here: Forecasting: principles and practice - 8 ARIMA models

Thanks!

Hoss Carroll

Customer Support Engineer

Alteryx

Alternewbie

Awesome -- thanks for the response and good to know I could monkey my way into an approximate solution! I've also submitted the request so hopefully this is something Alteryx could add in the future.

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