August 2020

Process Control, Instrumentation and Automation

Avoid common data preparation mistakes to improve analytics results

Time series data preparation in process manufacturing applications presents complex challenges, such as differences in data sampling rates, inconsistent or custom units, and the need to access data in multiple systems, among other issues.

Reckamp, J., Seeq Corp.

Time series data preparation in process manufacturing applications presents complex challenges, such as differences in data sampling rates, inconsistent or custom units, and the need to access data in multiple systems, among other issues. Therefore, time series data is very difficult to collate and align for modeling, analytics or other approaches commonly used to create insights. Modeling relates to a variety of techniques, including regression or clustering algorithms, along with more complex machine learning or artificial intelligence models, such as neural networks or random forest decision trees. Regardless of what type of model is chosen, all of them require data preparation to achiev

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