# "Prediction of Time Series Data"

Member Posts: 4 Learner I
edited May 2019 in Help
Hi all,

I am trying to use the auto model to predict my price over time. However, when the results are out, i realise that it does not take in the time series element of the data and provide me with the predictions based on the dates i produce.

Can I check will RapidMiner be able to take in the time series elements and provide me with the predictions based on the dates i have provided?

Please advice!
Thanks!

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## Answers

• Member Posts: 4 Learner I
Hi @David_A

Thank you for explaining! Can I also check under the forecast validation operation, what do they mean by window size, step size and horizon size? Do they differ? Because the default input, give me a super large root mean square error of a few billions. I do not understand from the video too. Anyone can help?

Please advice. Thank you very much in advance!

Regards
Judy
• Member Posts: 84  Guru
Hallo Judy,
Please, have a look at:
It is a RapidMiner training on time series, horizon, Arima.
Bon courage,
Maerkli

• RapidMiner Certified Analyst, Member Posts: 133   Unicorn
window: size how many examples are going to be used while performing the data transformation. For example is you have daily data and you want to transform it to weekly data you will put a 7
step size: how far should the forecast is going to predict you wan´t to take information to predict one week ahead? two weeks ahead? the model will try to understand how the information you input is affecting the outcome
horizon size: How many predictions you wan't only the next week or the next n weeks. The bigger the number in hear the greater the loss of performance.

• Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,497   Unicorn
Actually @MarcoBarradas there are just a couple of small corrections here.
I believe step size in the current operator determines how far to increment when constructing each window.  So for example, if you have the series {1,2,3,4,5,6,7,8,9} and you set the window size to 3 and the step size to 1, you would get: {1,2,3} then {2,3,4} then {3,4,5} and so on.  But if you set the step size to 3 then you would get {1,2,3} then {4,5,6} and so on.  So this determines how much your windows will overlap.
The horizon size is actually the definition you have given for step size, the number of periods in the future that is taken as the label for each window.

Brian T.
Lindon Ventures
Data Science Consulting from Certified RapidMiner Experts
• RapidMiner Certified Analyst, Member Posts: 133   Unicorn
@Telcontar120my bad LOL sorry for that I tried to define all by memory but actually by checking my latest process I can see I was terribly wrong.
But I have know a question then forecast horizon and from the apply forecast  and horizon size from Process Window are not equivalent. I tried to define it by what I understood while applying a forecast.
I have another question related with Process Window.
If I have daily information of sales and I want to predict next years sales by Month or by Week what would be the ideal window size since months are not standard? I remember form my business classes that we take months as 30 days and years as 360 days but I'm not sure if this would be translated the same way while trying to predict sales per month.
What I did to avoid my confusion was applying a Date to Nominal with yyyy-MM then aggregate grouping by my new Year-Month column and then analyzed it. But when I use Holt-Winters I'm no longer able to use the has indices.
Any suggestions?
• Administrator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, RMResearcher, Member Posts: 141  RM Research
Hi @MarcoBarradas,

Your definition of horizon size is correct. It is the number of predictions you want to make (so the number of values in the horizon). The step size is, as @Telcontar120 correctly stated, the increment between two windows. What you are asking for is called horizon offset. This is the number of values between end of window and begin of horizon (so for horizon offset = 0 it is the next value). For example if you have several weeks of daily data and the training window is from Monday to Sunday and you want to predict the values next week Sunday and Monday you would have horizon offset = 6 (6 days between end of training window and begin of horizon) and a horizon size = 2 (2 days to predict).

To keep your date information, just check 'keep old attribute' in the Date to Nominal operator and also aggregate the old date attribute, using for example max as the aggregation function.

You could also perform a forecast on the daily data, forecasting 30 days and aggregating the forecasted results to a monthly data. This may improve the performance, cause the forecast model can include the more granular daily data.

Hopes this helps,
Best regards,
Fabian
• Member Posts: 1 Newbie
Hi Fabian,

this is Stefan from Athens.
I am playing around with the example, that is meantioned in this thread. I wonder, if there is any operator, i can add to this example, that gives me some statistics and measures about the quality of the forecast.

Best, Stefan
• Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 2,515  RM Data Scientist
Hi Stefan,
have a look at the Operator "Forecast Validation" and its tutorial process. That should do the trick.
BR,
Martin
- Head of Data Science Services at RapidMiner -
Dortmund, Germany
• Administrator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, RMResearcher, Member Posts: 141  RM Research
Hi Stefan,

Nice to see that you found the way in the community. As Martin already stated, you can use the Forecast Validation operator.

Best regards,
Fabian
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