Time series data analysis

A processor-based system, comprising:

• one or more processing components;
• one or more memory or storage components encoding routines executable by the one or more processing components, wherein the routines, when executed, causes actions to be performed comprising:
• receiving as an input a set of time series data comprising a series of time values corresponding to one or more types of operations performed on an information technology (IT) platform;
• determining a plurality of time difference values for the set of time series data, wherein each time difference value comprises the time between different sequential operations performed on the IT platform;
• in an iterative loop:
• characterizing a set of clusters;
• sorting the time difference values into respective clusters of the set of clusters;
• and
• processing the respective clusters to remove clusters containing outlier time difference values;
• exiting the iterative loop when no further clusters are determined to be removable or a number of total time difference values fall below a threshold value, wherein an output from the iterative loop upon exiting comprises a pre-processed dataset;
• configuring one or more time series analytic routines based on the pre-processed dataset to specify a final dataset;
• processing the final data set using at least two time series analytic routines each generating a respective trained model;
• calculating one or more measures of a fit of each respective trained model;
• and
• selecting a final trained model for predicting future requests of the one or more types of operation based on the one or more measures of the fit for each respective trained model.

A method for selecting a model for predicting information technology (IT) operations, comprising:

• pre-processing a set of time series data to derive a pre-processed dataset by performing acts of:
• in an iterative loop:
• characterizing a set of clusters;
• sorting a plurality of time difference values into respective clusters of the set of clusters;
• and
• processing the respective clusters to remove clusters containing outlier time difference values;
• and
• exiting the iterative loop when no further clusters are determined to be removable or a number of total time difference values fall below a threshold value, wherein an output from the iterative loop upon exiting comprises the pre-processed dataset
• processing the pre-processed data set comprising time difference values using at least two time series analytic routines, each generating a respective trained model, wherein each time difference value of the time difference values comprises the time between different sequential operations performed on an IT platform;
• for each respective trained model, calculating one or both of root mean squared error (RMSE) or mean absolute error (MAE);
• comparing the RMSE or MAE for each respective trained model;
• and
• based on the comparison of the RMSE or MAE, selecting a final trained model for predicting future requests of one or more types of operations to be performed on the IT platform.