US 2017/0322877 A1 · MEMORY USAGE DETERMINATION TECHNIQUES

Abstract

Embodiments provide techniques for estimating seasonal indices for multiple periods. Some embodiments can receive a signal comprising a plurality of measures sampled over a span of time from an environment in which one or more processes are being executed. Some embodiments may then extract a seasonal effector and a de-seasonalized component from the signal. Next, some embodiments can apply one or more spline functions to the seasonal effector to generate a first model. Some embodiments may then apply a linear regression technique to the de-seasonalized component to generate a second model. Some embodiments may then initiate actions associated with the code. Some embodiments may then generate a forecast of the signal based on the first model and the second model. Next, some embodiments may initiate, based at least in part on the forecast, one or more actions associated with the environment.

Claims20

Dependent claims

A computer-implemented method comprising:

receiving, by one or more computer systems, a signal comprising a plurality of measures sampled over a span of time from an environment in which one or more processes are being executed;
extracting a seasonal effector and a de-seasonalized component from the signal;
applying one or more spline functions to the seasonal effector to generate a first model;
applying a linear regression technique to the de-seasonalized component to generate a second model;
generating a forecast of the signal based on the first model and the second model;
and
initiating, based at least in part on the forecast, one or more actions associated with the environment.

4 more elements

A system comprising:

one or more processors;
and
a memory accessible to the one or more processors, the memory storing one or more instructions that, upon execution by the one or more processors, causes the one or more processors to:
receive a signal comprising a plurality of measures sampled over a span of time from an environment in which one or more processes are being executed;
extract a seasonal effector and a de-seasonalized component from the signal;
apply one or more spline functions to the seasonal effector to generate a first model;
apply a linear regression technique to the de-seasonalized component to generate a second model;
generate a forecast of the signal based on the first model and the second model;
and
initiate, based at least in part on the forecast, one or more actions associated with the environment.

7 more elements

Dependencies are inferred from the claim text.

Memory usage determination techniques

Life of this patent

Memory usage determination techniques

Life of this patent

Abstract

Claims20