Time Series Data Multi-Class Classification
Your time intervals seem irregular. For most examples of time series, we have observations at fixed intervals, such as GDP every quarter. You can instead just create a regular machine learning model. If you think there is some sort of time component where you would not want January data to make predictions for November data, then you can manually create your cross validation partitions instead of creating them randomly. 

However, you are predicting 1, 2, or 3 based on low, medium, or high DaysInDuration. A classifier would not know that low and high are farther apart than low and medium. You can instead create a regression model that predicts DaysInDuration as your target feature. Once you have your prediction (e.g. 31), you can act accordingly whether the predictions is low, medium, or high.

Finally, you wouldn't be able to use DaysInDuration as a feature to predict the Classification_Label because it would give you the perfect answer every time. That would be target leakage.