Issue when deploying Improving Forecast Accuracy with Machine Learning example

[Weixin97]

I am having the below issue when trying to deploy the example given.

There was an error running the forecast for nyctaxi_weather_auto

Message: An error occurred (InvalidInputException) when calling the CreatePredictor operation: The attribute(s) [day_hour_name] present in the RELATED_TIME_SERIES schema should be of numeric type such as `integer` or `float`, or be added as a forecast dimension

Details: (caught InvalidInputException)

  File "/var/task/shared/helpers.py", line 66, in wrapper
    (status, output) = f(event, context)

  File "/var/task/create_predictor.py", line 40, in handler
    predictor.create()

  File "/var/task/shared/Predictor/predictor.py", line 228, in create
    self.cli.create_predictor(**self._create_params())

  File "/opt/python/botocore/client.py", line 386, in _api_call
    return self._make_api_call(operation_name, kwargs)

  File "/opt/python/botocore/client.py", line 705, in _make_api_call
    raise error_class(parsed_response, operation_name)

I think this is due to that the related dataset only accept additional columns with int / float type. Is there any hints on troubleshooting this on the py file in lambda function? Hope to get some help soon!

[czlaugh]

Hello. Your theory looks correct, related time series -- apart from keys and dimensions should be float or int type. Here, the day_hour_name is Monday_1, etc. First, I'd like to rule out that the original intent for date_hour_name wasn't a dimension -- in which case it should be in a target time series also. Can you direct me to the URL you were working from (line number) where you found this issue? I find the string in a few places, so checking to see where you were specifically to make the best judgement call on how to remedy. It could be changing the RTS or TTS depending. Thank you.