Forecasting device time series data during a cold-start period
AssigneeHewlett Packard Enterprise Development LP
InventorsMantej Singh Gill, Arun Mahendran, Rajendra Babu Godla, Tarun Khaneja, Shaunak Sensarma, Mahalakshmi L
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Abstract
Systems and methods are provided for reducing or eliminating a cold start period experienced with a first machine learning model by implementing a second machine learning model to generate forecasts during the first model's cold start period. The second machine learning model may be trained on data that is collected absent the new device or application, and generate forecasts that may be anonymized and generalized for several environments.
Claims20
A computer-implemented method comprising:
- executing a first machine learning model in a computer environment, the first machine learning model forecasting operations of a new device or application that is activated in the computer environment;
- in response to executing the first machine learning model and receiving time series data associated with the new device or application:
- splitting the time series data into a first portion of time series data and a second portion of time series data,
- providing the first portion of time series data to the first machine learning model for forecasting the operations of the new device or application, and
- training a second machine learning model using the first portion of the time series data concurrently with the first machine learning model forecasting the operations of the new device or application;
- comparing forecasts regarding the second portion of time series data generated by the first machine learning model and the second machine learning model;
- and
- in response to the comparison, utilizing the second machine learning model to forecast new operations of the new device or application.
A non-transitory computer-readable storage medium storing a plurality of instructions executable by a processor, the plurality of instructions when executed by the processor cause the processor to:
- execute a first machine learning model in a computer environment, the first machine learning model forecasting operations of a new device or application that is activated in the computer environment;
- in response to executing the first machine learning model and receiving time series data associated with the new device or application:
- split the time series data into a first portion of time series data and a second portion of time series data,
- provide the first portion of time series data to the first machine learning model for forecasting the operations of the new device or application, and
- train a second machine learning model using the first portion of the time series data concurrently with the first machine learning model forecasting the operations of the new device or application;
- compare forecasts regarding the second portion of time series data generated by the first machine learning model and the second machine learning model;
- and
- in response to the comparison, utilize the second machine learning model to forecast new operations of the new device or application.
A server comprising:
- a memory storing instructions;
- and
- a processor communicatively coupled to the memory and configured to execute the instructions to:
- execute a first machine learning model in a computer environment, the first machine learning model forecasting operations of a new device or application that is activated in the computer environment;
- in response to executing the first machine learning model and receiving time series data associated with the new device or application:
- split the time series data into a first portion of time series data and a second portion of time series data,
- provide the first portion of time series data to the first machine learning model for forecasting the operations of the new device or application, and
- train a second machine learning model using the first portion of the time series data concurrently with the first machine learning model forecasting the operations of the new device or application;
- compare forecasts regarding the second portion of time series data generated by the first machine learning model and the second machine learning model;
- and
- in response to the comparison, utilize the second machine learning model to forecast new operations of the new device or application.
Dependencies are inferred from the claim text.