How to use neural network for predicting future values?

Qisthi

hello, I am new to RapidMiner and i need some help.

for past few days I use RapidMiner to optimized the weights in neural network using a genetic algorithm. in this case i use neural network for prediction. and I want to see the results of prediction for some future values. there are some problem :
a. for example I have historical data from 2001 to 2010 and I want to see the results predicted in 2011 until 2015
b. i have 5 input and 1 input for the neural network. the data type is numerical.
i show you my xml file that formed when I use RapidMiner. it just shows the RMSE of my data when its treated using neural network based on genetic algorithm.

 <?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.3.015">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.3.015" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="5.3.015" expanded="true" height="60" name="Retrieve ANN1" width="90" x="112" y="75">
        <parameter key="repository_entry" value="//Local Repository/Tugas Akhir/ANN1"/>
      </operator>
      <operator activated="true" class="x_validation" compatibility="5.3.015" expanded="true" height="112" name="Validation" width="90" x="380" y="120">
        <parameter key="sampling_type" value="shuffled sampling"/>
        <process expanded="true">
          <operator activated="true" class="neural_net" compatibility="5.3.015" expanded="true" height="76" name="Neural Net" width="90" x="112" y="30">
            <list key="hidden_layers">
              <parameter key="one" value="5"/>
            </list>
            <parameter key="training_cycles" value="850"/>
            <parameter key="learning_rate" value="0.1"/>
            <parameter key="momentum" value="0.9"/>
          </operator>
          <connect from_port="training" to_op="Neural Net" to_port="training set"/>
          <connect from_op="Neural Net" from_port="model" to_port="model"/>
          <portSpacing port="source_training" spacing="0"/>
          <portSpacing port="sink_model" spacing="0"/>
          <portSpacing port="sink_through 1" spacing="0"/>
        </process>
        <process expanded="true">
          <operator activated="true" class="apply_model" compatibility="5.3.015" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
            <list key="application_parameters"/>
          </operator>
          <operator activated="true" class="performance" compatibility="5.3.015" expanded="true" height="76" name="Performance" width="90" x="179" y="30"/>
          <connect from_port="model" to_op="Apply Model" to_port="model"/>
          <connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
          <connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
          <connect from_op="Performance" from_port="performance" to_port="averagable 1"/>
          <portSpacing port="source_model" spacing="0"/>
          <portSpacing port="source_test set" spacing="0"/>
          <portSpacing port="source_through 1" spacing="0"/>
          <portSpacing port="sink_averagable 1" spacing="0"/>
          <portSpacing port="sink_averagable 2" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Retrieve ANN1" from_port="output" to_op="Validation" to_port="training"/>
      <connect from_op="Validation" from_port="model" to_port="result 1"/>
      <connect from_op="Validation" from_port="training" to_port="result 2"/>
      <connect from_op="Validation" from_port="averagable 1" to_port="result 3"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
      <portSpacing port="sink_result 3" spacing="0"/>
      <portSpacing port="sink_result 4" spacing="0"/>
    </process>
  </operator>
</process>

sorry for my english and thank you.

homburg

Hi Qisthi,

so far your process looks OK. But how does the structure of your data look like? You mentioned training and test data based on a time frame. What is it that you want to achieve? Do you want to perform predictions based on a regression analysis or do a time series forecast?

Cheers,
Helge

Qisthi

thank you for your reply. i want to perform prediction based on time series forecast. actually, i want to predict CO2 prediction at some factory. and i want to know with my historical data how much co2 will be produced in the future. so my structure data is consist of 5 attributes that influence CO2 production in that factory and 1 output in this case is co2