TrendMiner is a self-service data analytics tool that serves the process industry by providing a platform for engineers, operators and others to perform analytics of the time-series data collected by sensors and stored in data historians.

The notebook functionality enables the power of python for performing advanced analytics on your time-series data complementing the built-in functionalities available in TrendMiner. Create your own visualizations and deploy the output of the notebook to DashHub or build your own models using common machine learning techniques.

Use the code snippets found in this respository as inspiration for your own projects. You will find some examples of how Pandas, Numpy, ScikitLearn, Matplolib, Plotly and other packages can be used together with the time series data that is already available in TrendMiner to enhance your analysis.

Review the wiki page for more information on how to get started and a brief description of the snippets found in this repository.