Hi Devon,

The two pink lines are really important for detecting outliers :) They are the standard deviation lines; the default is 3 sigma, which is a simple method of detecting outliers: any points outside these lines are outliers and you should remove them if they are impacting negatively on your modeling. Through the context menu you can choose to plot 1, 2 or 3 stdev lines. The yellow line is the average, which you can also switch on and off through the context menu.

Now, to remove the outliers, you don’t have a fully automated process yet, but after you spot them using the charts, you then can delete them through the Delete Records Window (it’s accessible through the Data Menu). So it’s fairly simple and fast.

<<In addition, where it says POINT, can you explain how the two outlier drop downs options work (Outliers Error Perc and Outliers Abs Error) and what role does the value input box provide to these two options ?>>

These outliers refer to model output outliers (they are relative to the active model, so you can do interesting analyses with this). In the input box you write the error value you want. There’s also a tooltip over this input box that then tells you how many outliers fit that criterion; they then are shown in red in the charts (line charts and scatter plot).

Candida

That’s already on the wish list. We’ll add support for record editing in v6.

<< Question, would deleting rows make any difference modeling sequential time series in any of the non time series functions (regression, classification etc)? Are these modeling types even "aware" of the previous or next rows within the seguence? If not, I guess deleting a row would have little impact.>>

For classification and logistic regression, the answer is no. For regression the answer is also no for most fitness functions. But we now have new fitness functions for regression (and time series prediction too, but the deletion of records in TSP is not supported) that explore in simple ways the time sequence: the Trends series (4 fitness functions) and the Weighted series (9 functions). But even these fitness functions will work fine even if you randomize or delete the inputs, as the time component is just one of the terms.

And before you ask, we will implement the Trends and Weighted series for classification and logistic regression because I see how they can be useful for the kind of datasets you’re dealing with.

Candida

I see the Weighted MSE ...etc functions. Those make sense as in .. the most recent data is more "important" than older data I assume ,like a weighted moving avg... but the

trends fitness functions... Trends with Punishment ??? Defaulted to -10..Is there a simple high level explanation for the "Punishment" property ?? My imagination is running rampant..

(You're probably thinking  "Yes Devon, Punishment is having to respond to all your endless questions! " :)
Have no fear - I am running out of topics/questions to ask about!! :>

"pesky outliers" - that's a very polite way of characterizing them :>

Will download and test this weekend Jose   - Tx !

@Candida - I can actually understand that!! Will be experimenting with these all weekend. Thx

Questions

When installing new updates, is it recommended to uninstall the original install or can I just "ride over it" with the new build ?

Also - Will there, or is it even possible,  for the Time Series Run Category to  ever be able to allow more than a single column/ variable or is it fix to a single column due to the nature of the algo ?

I'd love to be able to experiment with that model Category  with multi input variables - especially for market data where attributes like Volume are very important and ..is there a clever work around  for creating this for the current live version  until that , if even possible, it became a built in feature in a future version.