Analysis and normalization of instantaneous data

Hello

Thank you so much for your help

I have my data in this way

It's time to be in a pillar

Insert x in a column and type y in a column

like this:

  time    x     y
-----------------------

   21    45     8
   35    52   12

Now I do not know how to normalize

And I can predict values of x, y at a later time?

Or do I analyze the data?

If anyone has experience

Maybe help

Thankful

Hello

 I am a beginner in this case

May you give me more guidance?

Tutorial to introduce me?

Thank you so much

Hello

thanks for your help

I searched a lot about the time series

But it is still ambiguous to me

My data is as below.

I do not know how to normalize the data in the RapidMiner program. And does not need normalization at all?

How to stack the series?

I ask you to help me

Thankful
best regard

Hallo student_compute,

Take 15mn to look this training

How to normalize data in RapidMiner by Markus Hofmann.

I enclose as well an example given some months ago in the RM Forum:

<?xml version="1.0" encoding="UTF-8"?><process version="9.1.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="9.1.000" expanded="true" name="Process">
    <parameter key="logverbosity" value="init"/>
    <parameter key="random_seed" value="2001"/>
    <parameter key="send_mail" value="never"/>
    <parameter key="notification_email" value=""/>
    <parameter key="process_duration_for_mail" value="30"/>
    <parameter key="encoding" value="SYSTEM"/>
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="9.1.000" expanded="true" height="68" name="Retrieve Sonar" width="90" x="45" y="85">
        <parameter key="repository_entry" value="//Samples/data/Sonar"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="9.1.000" expanded="true" height="82" name="Set Role" width="90" x="179" y="85">
        <parameter key="attribute_name" value="class"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles"/>
      </operator>
      <operator activated="true" class="concurrency:cross_validation" compatibility="9.1.000" expanded="true" height="145" name="Cross Validation" width="90" x="380" y="85">
        <parameter key="split_on_batch_attribute" value="false"/>
        <parameter key="leave_one_out" value="false"/>
        <parameter key="number_of_folds" value="10"/>
        <parameter key="sampling_type" value="automatic"/>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
        <parameter key="enable_parallel_execution" value="true"/>
        <process expanded="true">
          <operator activated="true" class="normalize" compatibility="9.1.000" expanded="true" height="103" name="Normalize" width="90" x="112" y="136">
            <parameter key="return_preprocessing_model" value="false"/>
            <parameter key="create_view" value="false"/>
            <parameter key="attribute_filter_type" value="all"/>
            <parameter key="attribute" value=""/>
            <parameter key="attributes" value=""/>
            <parameter key="use_except_expression" value="false"/>
            <parameter key="value_type" value="numeric"/>
            <parameter key="use_value_type_exception" value="false"/>
            <parameter key="except_value_type" value="real"/>
            <parameter key="block_type" value="value_series"/>
            <parameter key="use_block_type_exception" value="false"/>
            <parameter key="except_block_type" value="value_series_end"/>
            <parameter key="invert_selection" value="false"/>
            <parameter key="include_special_attributes" value="false"/>
            <parameter key="method" value="Z-transformation"/>
            <parameter key="min" value="0.0"/>
            <parameter key="max" value="1.0"/>
            <parameter key="allow_negative_values" value="false"/>
          </operator>
          <operator activated="true" class="h2o:logistic_regression" compatibility="9.0.000" expanded="true" height="124" name="Logistic Regression" width="90" x="246" y="34">
            <parameter key="solver" value="AUTO"/>
            <parameter key="reproducible" value="false"/>
            <parameter key="maximum_number_of_threads" value="4"/>
            <parameter key="use_regularization" value="false"/>
            <parameter key="lambda_search" value="false"/>
            <parameter key="number_of_lambdas" value="0"/>
            <parameter key="lambda_min_ratio" value="0.0"/>
            <parameter key="early_stopping" value="true"/>
            <parameter key="stopping_rounds" value="3"/>
            <parameter key="stopping_tolerance" value="0.001"/>
            <parameter key="standardize" value="true"/>
            <parameter key="non-negative_coefficients" value="false"/>
            <parameter key="add_intercept" value="true"/>
            <parameter key="compute_p-values" value="true"/>
            <parameter key="remove_collinear_columns" value="true"/>
            <parameter key="missing_values_handling" value="MeanImputation"/>
            <parameter key="max_iterations" value="0"/>
            <parameter key="max_runtime_seconds" value="0"/>
          </operator>
          <connect from_port="training set" to_op="Normalize" to_port="example set input"/>
          <connect from_op="Normalize" from_port="example set output" to_op="Logistic Regression" to_port="training set"/>
          <connect from_op="Normalize" from_port="preprocessing model" to_port="through 1"/>
          <connect from_op="Logistic Regression" from_port="model" to_port="model"/>
          <portSpacing port="source_training set" spacing="0"/>
          <portSpacing port="sink_model" spacing="0"/>
          <portSpacing port="sink_through 1" spacing="0"/>
          <portSpacing port="sink_through 2" spacing="0"/>
        </process>
        <process expanded="true">
          <operator activated="true" class="apply_model" compatibility="9.1.000" expanded="true" height="82" name="Apply Model" width="90" x="112" y="85">
            <list key="application_parameters"/>
            <parameter key="create_view" value="false"/>
          </operator>
          <operator activated="true" class="apply_model" compatibility="9.1.000" expanded="true" height="82" name="Apply Model (2)" width="90" x="246" y="34">
            <list key="application_parameters"/>
            <parameter key="create_view" value="false"/>
          </operator>
          <operator activated="true" class="performance" compatibility="9.1.000" expanded="true" height="82" name="Performance" width="90" x="380" y="34">
            <parameter key="use_example_weights" value="true"/>
          </operator>
          <connect from_port="model" to_op="Apply Model (2)" to_port="model"/>
          <connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
          <connect from_port="through 1" to_op="Apply Model" to_port="model"/>
          <connect from_op="Apply Model" from_port="labelled data" to_op="Apply Model (2)" to_port="unlabelled data"/>
          <connect from_op="Apply Model (2)" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
          <connect from_op="Performance" from_port="performance" to_port="performance 1"/>
          <connect from_op="Performance" from_port="example set" to_port="test set results"/>
          <portSpacing port="source_model" spacing="0"/>
          <portSpacing port="source_test set" spacing="0"/>
          <portSpacing port="source_through 1" spacing="0"/>
          <portSpacing port="source_through 2" spacing="0"/>
          <portSpacing port="sink_test set results" spacing="0"/>
          <portSpacing port="sink_performance 1" spacing="0"/>
          <portSpacing port="sink_performance 2" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Retrieve Sonar" from_port="output" to_op="Set Role" to_port="example set input"/>
      <connect from_op="Set Role" from_port="example set output" to_op="Cross Validation" to_port="example set"/>
      <connect from_op="Cross Validation" from_port="model" to_port="result 3"/>
      <connect from_op="Cross Validation" from_port="test result set" to_port="result 1"/>
      <connect from_op="Cross Validation" from_port="performance 1" to_port="result 2"/>
      <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>

Bonne chance,

Maerkli

In addition to the video and process kindly posted by @Maerkli, with time series data, you will need to know what first order differencing is and why you might need to use a moving average to de-trend your data. You will have to understand your data first so plot it out and take a look.

Hallo Hughes,

Thanks for the link.

Maerkli

PS. C'est du lourd.

Hello to all

Thank you very much for helping my dear friends  

I am a beginner in time series.

I studied the basic concepts

But it is difficult to understand and generalize the concepts of theory to practical

My data is related to a sensor that is received at different times.

I want to anticipate new values for later on these data

But do not know where to start

So, I asked experienced friends at the forum for help.

I'm sure to try to create a process. So friends can guide me.

Thank you all

good day

Hallo Student_compute,

If your data are not confidential, share them with the RapidMiner community and explain exactly what you want. I am sure that many people are going to help you out. CSV format is very convenient.

Maerkli

hello
I was busy with my exams for a while

My data is as follows

I want to analyze this data

But do not know how 

Do not I need to use clustering or classification or time series?

Can you help me solve this problem?

I need help

Thanks if you have any help 

Hello

Thank you so much for your reply

Yes . I want to analyze my data first. And say how data is.

Then, for future periods, I predict the quality and I can report the accuracy of the forecast. But do not know how And what operators should I do?

I do not know which operators and data mining algorithms I use to analyze this kind of data?

Please help my experienced friends present my example.

Thankful

Thanks for the good rapidminer and good friends

Hello

thanks for your help

I saw the links

But

Questions were made to me

Is it with this data? Can I predict the next value of quality at a later time by time series?

Is there a possibility of clustering?

In the links you introduced, I did not see the sample xml file. Is there a sample XML file for me?

I really need your help

Thank you

Hello

Dear friends and professors

I hope you are healthy

I read the following link below

https://community.rapidminer.com/discussion/52339/time-series-extension-features-of-version-0-1-2
And I tried to know and understand a lot.

But I could not get the result.

What exactly are binom, simple, and what is the purpose

That What are aic, bic, aicc values ​​in the output of samples in the rapidminer program? Great values ​​for them? Or small?

I know I have a lot of expectations.

But I do not know how to use the time series for their data and their future values?

Please guide me

Do you give me a useful link to know the concepts of time series and arima in rapidminer?

Thank you so much

And

I am waiting for your help

good day

Hello dear professor

Thank you very much for your help and links.

I can give you examples of this tutorial.

I am a beginner. Maybe you are a respected professor. Please If possible, depending on the data I sent. Send me a simple forecast sample using time series or Arima algorithm? How do you know the process?

I'm sorry for my request.

Thanks a lot

With respect

Hello

Be sure, dear professor

thank you

I study . I try . In the RapidMiner, I will create a process and send you a review

Thank you for guidance at that time.

May I send my email as a private message, so if I'm not in the forum, do I email?

Thankful

With respect

Hello

I tried hard to predict the future values for the quality variable in the RapidMiner

I will process my own, according to the data I have already provided. I created

I sent the results

But I got confused

I do not know which one is my prediction. And which one is correct and correct?

Why are some values "?" In the output?

How do I determine the best value for the Arima parameters?

Please guide my friends

I do not know the meaning of the graphs

Thankful

Hello

thanks for your response

Is my process right?

How do I find out which value is best for arima parameters?

Should the aic, bic, aicc values ​​be the lowest? These negative values ​​are obtained. It is true?

How to use the optimization operator to find the optimal values ​​for arima parameters?

And how can I use Svm, decision tree to predict future values ​​of variable quality and report accuracy of prediction and compare results with arima results?

Please guide

thanks a lot

..
And The book link you mentioned. I saw It is very crowded. And my time is low. I ask you to give me a brief summary, if possible, in which case I would like to thank you very much....