This workflow predicts the irregular component of time series (energy consumption) by an LSTM network with lagged values as input. The irregular component of time series is what is left after removing the trend and first and second seasonality. The trained model is then used for out-of-sample forecasting. The forecasted values are compared to the actual values, and the performance of the forecast is reported via scoring metrics and a line plot.
i am try run play this sample but still not working, hope someone here any idea how to solve this.
Thanks a lot,
Here some error message:
ERROR PurePythonNodeSetFactory Failed to parse Python node extension at path ‘D:\myAnalyticsGear\KNIME\plugins\graudata.mdh_extension_1.0.0.202308210900\src\main\python’.
An exception was raised by the Python Proxy. Return Message: Traceback (most recent call last):
File “D:\myAnalyticsGear\KNIME\plugins\graudata.mdh_extension.channel.bin.win32.x86_64_1.0.0.202308210900\env\Lib\site-packages\py4j\clientserver.py”, line 617, in _call_proxy
return_value = getattr(self.pool[obj_id], method)(*params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “D:\myAnalyticsGear\KNIME\plugins\org.knime.python3.nodes_5.1.1.v202309080842\src\main\python_node_backend_launcher.py”, line 841, in loadExtension
raise RuntimeError(
RuntimeError: Failed to load extension graudata.mdh_extension from extension with error: Traceback (most recent call last):
File “D:\myAnalyticsGear\KNIME\plugins\org.knime.python3.nodes_5.1.1.v202309080842\src\main\python_node_backend_launcher.py”, line 836, in loadExtension
importlib.import_module(extension_module)
ModuleNotFoundError: No module named ‘grp’
WARN MoePreferencePage Path to MOE not set in Preferences. Please check!
ERROR PurePythonNodeSetFactory Failed to parse Python node extension at path ‘D:\myAnalyticsGear\KNIME\plugins\graudata.mdh_extension_1.0.0.202308210900\src\main\python’.
An exception was raised by the Python Proxy. Return Message: Traceback (most recent call last):
File “D:\myAnalyticsGear\KNIME\plugins\graudata.mdh_extension.channel.bin.win32.x86_64_1.0.0.202308210900\env\Lib\site-packages\py4j\clientserver.py”, line 617, in _call_proxy
return_value = getattr(self.pool[obj_id], method)(*params)
…
none of the errors you posted seem to have anything to do with the workflow you posted. Its most advanced extensions it uses are the Deep Learning extensions (see an overview of the used extensions on the workflow’s Hub space).
Please post a screenshot of the workflow showing which node actually fails and post an error log of that node.
Two things I see could be an issue:
possibly there is now input file given, that is what did not work for me on a quick try. Maybe @Maarit could help here?
The Deep Learning extensions need some setup, you might want to read some blog articles or Mr. Laubers (@mlauber71) Medium articles to get them set up correctly.
