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Thank you for this amazing library that helps in extracting features for time series data.
I have studied the documentation for structuring data in a format that tsfresh can understand, but I believe I have formatted it correctly. I know that the data associated with each ID at each time is layered on top of one another. Based on the robot failure example, it is expected that each robot (with an ID of 1 to 88) represents either a successful execution with the label 'True' or an unsuccessful execution with the label 'False', but not both, given a different set of features. Each robot either tells if that was a successful execution or a failed execution.
How is a dataset in which a robot with ID-1 represents successful execution (True) with one set of features and failure execution (False) with another set of features supplied into tsfresh for feature extraction to do multi-label classification?
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Thank you for this amazing library that helps in extracting features for time series data.
I have studied the documentation for structuring data in a format that tsfresh can understand, but I believe I have formatted it correctly. I know that the data associated with each ID at each time is layered on top of one another. Based on the robot failure example, it is expected that each robot (with an ID of 1 to 88) represents either a successful execution with the label 'True' or an unsuccessful execution with the label 'False', but not both, given a different set of features. Each robot either tells if that was a successful execution or a failed execution.
How is a dataset in which a robot with ID-1 represents successful execution (True) with one set of features and failure execution (False) with another set of features supplied into tsfresh for feature extraction to do multi-label classification?
Could you please help me out with this?
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