Prediction (Forecasting ) with RM

Original message from SourceForge forum at http://sourceforge.net/forum/forum.php?thread_id=2019003&;forum_id=390413

Hi,
I have some electricity data for past 1 year.Using this data i want to predict data for next 1 month.Is it possible with RM.
can somebody help me in this regard.
Thanks in advance

Thanks,
Swapk


Answer by Ingo Mierswa:

Hello Swapk,

in principle, tasks like those is what RapidMiner was made for. You could use a windowing to window the past data and create prediction models on the windows for different prediction horizons. Then, all models are applied to the last available window and the predictions are appended to the series as prediction.

Cheers,
Ingo


Question by Gladys:

Hello Ingo:

How we can implement this windowing scheme in RapidMiner?  

Best Regards,

Gladys


Answer by Ingo:

Hi Gladys,

the basic idea is to use a windowing operator like in the following process:

<operator name="Root" class="Process" expanded="yes"> 
<operator name="ExampleSetGenerator" class="ExampleSetGenerator"> 
<parameter key="number_of_attributes" value="1"/> 
<parameter key="target_function" value="sum"/> 
</operator> 
<operator name="FeatureNameFilter" class="FeatureNameFilter"> 
<parameter key="filter_special_features" value="true"/> 
<parameter key="skip_features_with_name" value="label"/> 
</operator> 
<operator name="Series2WindowExamples" class="Series2WindowExamples"> 
<parameter key="series_representation" value="encode_series_by_examples"/> 
<parameter key="window_size" value="10"/> 
</operator> 
<operator name="XValidation" class="XValidation" expanded="yes"> 
<operator name="LibSVMLearner" class="LibSVMLearner"> 
<list key="class_weights"> 
</list> 
<parameter key="svm_type" value="epsilon-SVR"/> 
</operator> 
<operator name="OperatorChain" class="OperatorChain" expanded="yes"> 
<operator name="ModelApplier" class="ModelApplier"> 
<list key="application_parameters"> 
</list> 
</operator> 
<operator name="Performance" class="Performance"> 
</operator> 
</operator> 
</operator> 
</operator> 

There is also an operator for multivariate windowing and there are sliding window validations (backtesting) which are more appropriate for this type of analysis. Just try to play around in this field!

Cheers,
Ingo