ARIMA loop model get stucked always and do not complete the execution
data1maths
New Altair Community Member
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.
Tagged:
0
Answers
-
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
1 -
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 # rm_main is a mandatory function, # the number of arguments has to be the number of input ports (can be none) DT = %{dateTime} def rm_main(data): data[DT] = pd.to_datetime(data[DT]) data = data.set_index(DT) data = data.resample('4H', how='count') data = data.reset_index() #data.rm_metadata["Datetime"] = (None,"regular") # connect 2 output ports to see the results 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>0
