Analysis and normalization of instantaneous data

student_computestudent_compute Member Posts: 56 Contributor I
Hello friends
I have a sensor that gives me information at any time (10 milliseconds once). E.g. x, y I have thousands of these x and y. I know clustering and classification in rapidminer.
I ask experienced friends
What suggestions do you have for this data?
How can I predict x, y?
And analyze the data?
I ask you to help me
Thanks to the very good rapidminer



Answers

  • Telcontar120Telcontar120 Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,048   Unicorn
    You are going to want to look at some kind of feature selection.  I would recommend one of the variance reduction techniques, like PCA.  You will have a lot of redundant overlap in data that is taken so frequently.
    What is it that you are trying to do with this data---predict some outcome?
    Brian T.
    Lindon Ventures 
    Data Science Consulting from Certified RapidMiner Experts
  • student_computestudent_compute Member Posts: 56 Contributor I
    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
  • Telcontar120Telcontar120 Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,048   Unicorn
    Take a look at the new Time Series operators, they are part of the standard Studio operator set.
    There is an operator for Normalizing time series data.  There are also operators for forecasting time series data such as ARIMA or Holt-Winters.  I would probably start with ARIMA.

    Brian T.
    Lindon Ventures 
    Data Science Consulting from Certified RapidMiner Experts
  • student_computestudent_compute Member Posts: 56 Contributor I
    Hello
     I am a beginner in this case
    May you give me more guidance?
    Tutorial to introduce me?
    Thank you so much
  • hughesfleming68hughesfleming68 Member Posts: 156   Unicorn
    Take a look at the ARIMA examples, specifically the ARIMA model for Lake Huron. To better understand ARIMA, do a search on Rob Hyndman. He wrote the forecast package for R and there are a lot of examples that you could duplicate in Rapidminer. You will have to understand normalization and what it means for your time series to be stationary. Don't take this part lightly as it can make or break your forecast.
    lionelderkrikorTelcontar120
  • student_computestudent_compute Member Posts: 56 Contributor I
    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?
    How to use ARIMA? So I can predict the x and y values at a later time?
    I ask you to help me
    Thankful
    best regard
  • hughesfleming68hughesfleming68 Member Posts: 156   Unicorn
    edited January 8
    Did you look at the operators to see what they do?  @student_compute, I have read a lot of your posts and you seem quite lost. Unfortunately,  there are no shortcuts. You have to put in the time to learn the material. Is this for school? There are already standard ARIMA examples. It would be helpful if you could be more specific about what exactly you are having difficulty with. Do you understand what normalization means? Do you understand why a time series might need to be de-trended? When your question is so broad, it is hard to figure out where to begin. Post a process. That is the best way to get help. It is much quicker to solve problems that way.
    MaerklilionelderkrikorsgenzerSGolbert
  • MaerkliMaerkli Member Posts: 84   Unicorn
    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


    hughesfleming68lionelderkrikorsgenzer
  • hughesfleming68hughesfleming68 Member Posts: 156   Unicorn
    edited January 8
    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.


    lionelderkrikorMaerklisgenzer
  • MaerkliMaerkli Member Posts: 84   Unicorn
    Hallo Hughes,
    Thanks for the link.
    Maerkli
    PS. C'est du lourd.

  • student_computestudent_compute Member Posts: 56 Contributor I
    Hello to all
    Thank you very much for helping my dear friends <3 
    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
  • MaerkliMaerkli Member Posts: 84   Unicorn
    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
  • student_computestudent_compute Member Posts: 56 Contributor I
    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  <3
  • Telcontar120Telcontar120 Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,048   Unicorn
    Are you trying to predict quality score as a function of time?  If so then try looking at the data with the time series operators.  You can plot this series and look at it using the Classic Decomposition operator or the Moving Average operator to detect patterns in the data.  Then you can choose an appropriate forecast method such as Holt Winters or ARIMA.



    Brian T.
    Lindon Ventures 
    Data Science Consulting from Certified RapidMiner Experts
  • student_computestudent_compute Member Posts: 56 Contributor I
    Hello
    Thank you so much for your reply <3
    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
  • sgenzersgenzer Administrator, Moderator, Employee, RapidMiner Certified Analyst, Community Manager, Member, University Professor Posts: 2,130  Community Manager
    @student_compute sorry but we've gone over this many times. You MUST learn how to post your XML and your data sets on this forum: https://community.rapidminer.com/discussion/37047.

    Others - you are all too kind. Please note. :neutral:

    Scott

  • student_computestudent_compute Member Posts: 56 Contributor I
    Yes . You are right.
    This is an example of my data

    But I'm sorry to say that. I really do not know how to use the time series for analysis and forecasting. I searched in the forum but I do not know how to do it for my data? :(
    I know there is a lot of demand and I ask the community to do it for me. I tried a lot. So I can do it myself. That I did not succeed.
    I request your dear friends, if possible, to help me once more.
    And provide a process example that will use the time series to analyze and predict my data.
    And can I use clustering, classification, or Associative rules mining? How?
    Thankful
    Sorry for the time of the forum

    Thanks for the good rapidminer and good friends <3
  • sgenzersgenzer Administrator, Moderator, Employee, RapidMiner Certified Analyst, Community Manager, Member, University Professor Posts: 2,130  Community Manager
    hello @student_compute - ok THANK YOU for your data. That helps. It looks to me like your data is very straightforward. Hence I would next strongly recommend going through these posts and following Dr. Temme's steps:

    https://community.rapidminer.com/discussion/41717/time-series-extension-release-of-the-alpha-version-0-1-2
    https://community.rapidminer.com/discussion/42585/time-series-extension-features-of-version-0-1-2

    note that the Time Series operators are no longer an extension; they are part of the core.

    Scott

    cc @eackley29
  • student_computestudent_compute Member Posts: 56 Contributor I
    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
  • student_computestudent_compute Member Posts: 56 Contributor I
    Hello
    Dear friends and professors
    I hope you are healthy
    I read the following link below
    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 <3
  • tftemmetftemme Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, RMResearcher, Member Posts: 68  RM Research
    Hi @student_compute,

    As the time series extension is now part of RM Core, you can find the examples mentioned in https://community.rapidminer.com/discussion/42585/time-series-extension-features-of-version-0-1-2 directly in RapidMiner in the Samples/Time Series folder in the repository panel (as well as some more templates showing the functionality added in later updates). 

    For simple and binom, these are only the names of two different kind of filter weights (simple = all weights the same; binom = expansion of binomial expression, example given in the thread).

    For AIC, BIC and AICc please have a look on the operator help text or this wikipedia link (https://en.wikipedia.org/wiki/Akaike_information_criterion).

    For a better understanding of time series analysis in general I would suggest this free online text book: https://otexts.com/fpp2/ (Though the author is not using RapidMiner, but still concepts are greatly explained).

    Best regards,
    Fabian

    sgenzer
  • student_computestudent_compute Member Posts: 56 Contributor I
    Hello dear professor
    Thank you very much for your help and links. :)
    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 <3
  • hughesfleming68hughesfleming68 Member Posts: 156   Unicorn

    tftemme
  • tftemmetftemme Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, RMResearcher, Member Posts: 68  RM Research
    edited March 11
    Hi @student_compute,

    The templates (of which @hughesfleming68 posted this nice screenshot, thanks by the way) and the free text book I linked, should give you enough insight into learning how to analyse time series data and create forecasts, also for your problems.

    By the way, I am in no way a professor, but thanks ;-)

    Best regards,
    Fabian
    sgenzer
  • student_computestudent_compute Member Posts: 56 Contributor I
    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
  • tftemmetftemme Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, RMResearcher, Member Posts: 68  RM Research
    Hello @student_compute

    As I said I am not a professor. 

    Nice to hear that I could help you. If you have further problems, feel free to ask here again in the community.

    Best regards,
    Fabian
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