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ARIMA loop model get stucked always and do not complete the execution

Hello everyone,

I've created a process to make forcast on a time series using arima(p,d,q) model inside a loop that changes p,d,q parameters(Optimize Parameters (Grid)) within a certain range. the problem is that once I enlarge the range of each parameter the execution don't provide forecast output anymore, which is really odd.

???

Here is an example proving that the execution don't provide results:

AND

I do really need your help guys.

Thank you in advance.

Best regards.

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tftemme

Hi @data1maths,

Would it be possible to provide your process (as xml) and best a sample dataset (or a one which is as similar as possible), to investigate in more detail what is happening?

Best regards,

Fabian

data1maths

Hi tftemme,

Here is the process(xml).

Thank you very much.

Best regards

<?xml version="1.0" encoding="UTF-8"?><process version="9.0.003">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="9.0.003" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="9.0.003" expanded="true" height="68" name="Retrieve Time series-mvts de congestion" width="90" x="112" y="136">
        <parameter key="repository_entry" value="Time series-mvts de congestion"/>
      </operator>
      <operator activated="true" class="set_macros" compatibility="9.0.003" expanded="true" height="82" name="Set Macros" width="90" x="380" y="34">
        <list key="macros">
          <parameter key="dateTime" value="'PILOT ON'"/>
        </list>
      </operator>
      <operator activated="true" class="generate_attributes" compatibility="9.0.003" expanded="true" height="82" name="Generate Attributes" width="90" x="313" y="289">
        <list key="function_descriptions">
          <parameter key="count" value="1"/>
        </list>
      </operator>
      <operator activated="true" class="select_attributes" compatibility="9.0.003" expanded="true" height="82" name="Select Attributes" width="90" x="447" y="289">
        <parameter key="attribute_filter_type" value="subset"/>
        <parameter key="attributes" value="count|PILOT ON"/>
      </operator>
      <operator activated="true" class="generate_id" compatibility="9.0.003" expanded="true" height="82" name="Generate ID" width="90" x="514" y="187"/>
      <operator activated="true" class="python_scripting:execute_python" compatibility="8.2.000" expanded="true" height="103" name="Execute Python" width="90" x="715" y="34">
        <parameter key="script" value="import pandas as pd&#10;&#10;# rm_main is a mandatory function, &#10;# the number of arguments has to be the number of input ports (can be none)&#10;DT = %{dateTime}&#10;&#10;def rm_main(data):&#10;&#10;  data[DT] = pd.to_datetime(data[DT])&#10;  data = data.set_index(DT)&#10;  data = data.resample('4H', how='count')&#10;  data = data.reset_index()&#10;    &#10;  #data.rm_metadata[&quot;Datetime&quot;] = (None,&quot;regular&quot;)&#10;    &#10;    # connect 2 output ports to see the results&#10;  return data"/>
      </operator>
      <operator activated="false" class="multiply" compatibility="9.0.003" expanded="true" height="82" name="Multiply" width="90" x="782" y="136"/>
      <operator activated="false" class="write_excel" compatibility="9.0.003" expanded="true" height="82" name="Write Excel" width="90" x="849" y="289">
        <parameter key="excel_file" value="C:\Users\HP\Desktop\tmpa\Mental strenght\dataset sample-RM-cong.xlsx"/>
      </operator>
      <operator activated="true" breakpoints="after" class="concurrency:optimize_parameters_grid" compatibility="9.0.003" expanded="true" height="166" name="Optimize Parameters (Grid)" origin="GENERATED_SAMPLE" width="90" x="916" y="34">
        <list key="parameters">
          <parameter key="ARIMA Original Data.plithiumorder_of_the_autoregressive_model" value="[1;24;24;linear]"/>
          <parameter key="ARIMA Original Data.dlithiumdegree_of_differencing" value="[0.0;2;2;linear]"/>
          <parameter key="ARIMA Original Data.qlithiumorder_of_the_moving-average_model" value="[0;24;24;linear]"/>
        </list>
        <process expanded="true">
          <operator activated="true" class="time_series:arima_trainer" compatibility="9.0.003" expanded="true" height="103" name="ARIMA Original Data" origin="GENERATED_SAMPLE" width="90" x="380" y="34">
            <parameter key="time_series_attribute" value="count"/>
            <parameter key="indices_attribute" value="id"/>
            <parameter key="plithiumorder_of_the_autoregressive_model" value="24"/>
            <parameter key="dlithiumdegree_of_differencing" value="2"/>
            <parameter key="qlithiumorder_of_the_moving-average_model" value="24"/>
          </operator>
          <connect from_port="input 1" to_op="ARIMA Original Data" to_port="example set"/>
          <connect from_op="ARIMA Original Data" from_port="forecast model" to_port="output 1"/>
          <connect from_op="ARIMA Original Data" from_port="performance" to_port="performance"/>
          <connect from_op="ARIMA Original Data" from_port="original" to_port="output 2"/>
          <portSpacing port="source_input 1" spacing="0"/>
          <portSpacing port="source_input 2" spacing="0"/>
          <portSpacing port="sink_performance" spacing="0"/>
          <portSpacing port="sink_model" spacing="0"/>
          <portSpacing port="sink_output 1" spacing="0"/>
          <portSpacing port="sink_output 2" spacing="0"/>
          <portSpacing port="sink_output 3" spacing="0"/>
        </process>
      </operator>
      <operator activated="true" class="time_series:apply_forecast" compatibility="9.0.003" expanded="true" height="82" name="Apply Forecast" origin="GENERATED_SAMPLE" width="90" x="1050" y="187"/>
      <connect from_op="Retrieve Time series-mvts de congestion" from_port="output" to_op="Set Macros" to_port="through 1"/>
      <connect from_op="Set Macros" from_port="through 1" to_op="Generate Attributes" to_port="example set input"/>
      <connect from_op="Generate Attributes" from_port="example set output" to_op="Select Attributes" to_port="example set input"/>
      <connect from_op="Select Attributes" from_port="example set output" to_op="Generate ID" to_port="example set input"/>
      <connect from_op="Generate ID" from_port="example set output" to_op="Execute Python" to_port="input 1"/>
      <connect from_op="Execute Python" from_port="output 1" to_op="Optimize Parameters (Grid)" to_port="input 1"/>
      <connect from_op="Multiply" from_port="output 1" to_op="Write Excel" to_port="input"/>
      <connect from_op="Optimize Parameters (Grid)" from_port="performance" to_port="result 3"/>
      <connect from_op="Optimize Parameters (Grid)" from_port="model" to_port="result 4"/>
      <connect from_op="Optimize Parameters (Grid)" from_port="parameter set" to_port="result 5"/>
      <connect from_op="Optimize Parameters (Grid)" from_port="output 1" to_op="Apply Forecast" to_port="forecast model"/>
      <connect from_op="Apply Forecast" from_port="example set" to_port="result 1"/>
      <connect from_op="Apply Forecast" from_port="original" 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"/>
      <portSpacing port="sink_result 5" spacing="0"/>
      <portSpacing port="sink_result 6" spacing="0"/>
      <portSpacing port="sink_result 7" spacing="0"/>
    </process>
  </operator>
</process>

dataset sample-RM-cong.csv