US 2025/0165822 A1Published application

Systems and methods for pre-selecting a machine learning model based on determined dataset characteristics

AssigneeCapital One Services, LLC

InventorsVannia GONZALEZ MACIAS, Justin ESSERT, Pratik GANDHI

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Abstract

Pre-selecting a machine learning model based on determined dataset characteristics may be facilitated. In some embodiments, a time-series dataset may be received by a system. Based on the time-series dataset, the system generates a periodogram to determine power frequencies of a set of frequencies of the time-series dataset. The system may then determine an asymptotic p-value based on the periodogram, where the asymptotic p-value indicates a probability value that a seasonal cycle is part of the time-series dataset. In response to a comparison between the asymptotic p-value and a threshold p-value indicating that the asymptotic p-value fails to exceed the threshold p-value, the system determines a seasonality cycle of the time-series dataset. The system then generates, for display, on a user interface, a recommendation for a machine learning model based on (i) the seasonality cycle of the time-series dataset and (ii) the power frequencies.

Claims20

Dependent claims

A system for pre-selecting a machine learning model based on determined dataset characteristics, the system comprising:

one or more processors executing computer program instructions that, when executed, cause operations comprising:
receiving a time-series dataset, the time-series dataset comprising a set of data points having a relationship between (i) a set of timestamps and (ii) a value associated with a variable;
generating a periodogram based on the time-series dataset to determine power frequencies of a set of frequencies of the time-series dataset, wherein the periodogram is a frequency domain representation of the time-series dataset;
determining an asymptotic p-value of the periodogram based on the power frequencies, wherein the asymptotic p-value of the periodogram indicates a probability value that a seasonal cycle is part of the time-series dataset;
in response to a comparison between the asymptotic p-value and a user-selected threshold p-value indicating that the asymptotic p-value fails to exceed the user-selected threshold value, determining a seasonality cycle of the time-series dataset;
in response to determining the seasonality cycle of the time-series dataset, determining a period associated with the seasonality cycle based on the power frequencies of the time-series dataset;
and
generating, for display, on a user interface, a recommendation for a machine learning model of a set of machine learning models based on (i) the seasonality cycle of the time-series dataset, (ii) the power frequencies of the set of frequencies of the time-series dataset, and (iii) the period associated with the seasonality cycle.

5 more elements

A method for pre-selecting a machine learning model based on determined dataset characteristics, the method comprising:

receiving a time-series dataset, the time-series dataset comprising a set of data points having a relationship between (i) a set of timestamps and (ii) a value associated with a variable;
generating a periodogram based on the time-series dataset to determine power frequencies of a set of frequencies of the time-series dataset, wherein the periodogram is a frequency domain representation of the time-series dataset;
determining an asymptotic p-value based on the periodogram, wherein the asymptotic p-value of the periodogram indicates a probability value that a seasonal cycle is part of the time-series dataset;
in response to a comparison between the asymptotic p-value and a threshold p-value indicating that the asymptotic p-value exceeds the threshold p-value, determining a seasonality cycle of the time-series dataset, wherein the comparison indicates that the asymptotic p-value fails to exceed the threshold p-value;
and
generating, for display, on a user interface, a recommendation for a machine learning model based on (i) the seasonality cycle of the time-series dataset and (ii) the power frequencies of the set of frequencies of the time-series dataset.

3 more elements

One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause operations comprising:

generating a periodogram based on a time-series dataset to determine power frequencies of a set of frequencies of the time-series dataset;
determining an asymptotic p-value based on the periodogram, wherein the asymptotic p-value of the periodogram indicates a probability value that a seasonal cycle is part of the time-series dataset;
in response to a comparison between the asymptotic p-value and a threshold p-value indicating that the asymptotic p-value fails to exceed the threshold p-value, determining a seasonality cycle of the time-series dataset;
and
generating, for display, on a user interface, a recommendation for a machine learning model based on (i) the seasonality cycle of the time-series dataset and (ii) the power frequencies of the set of frequencies of the time-series dataset.

2 more elements

Dependencies are inferred from the claim text.