IMPLEMENTING A COMPUTER SYSTEM TASK INVOLVING NONSTATIONARY STREAMING TIME-SERIES DATA BASED ON A BIAS-VARIANCE-BASED ADAPTIVE LEARNING RATE

摘要

A computer-implemented method for implementing a computer system task involving nonstationary streaming time-series data based on a bias-variance-based adaptive learning rate includes generating a parameter sequence including a plurality of parameters corresponding to respective iteration counts. Generating the parameter sequence includes obtaining a first parameter value corresponding to a given iteration count by calculating estimators of moments associated with an objective function corresponding to the given iteration count based on a second parameter value corresponding to a prior iteration count using a sequential mean tracking method, and obtaining the first parameter value by performing a step of a gradient descent method based on the calculated moments and the second parameter value. The method further includes learning a time-series model based on the parameter sequence, and implementing a computer system task using the time-series model.