EMD-Spectral Prediction (ESP)

• US 20160140584A1
• Filed: 11/17/2014
• Published: 05/19/2016
• Est. Priority Date: 11/17/2014
• Status: Abandoned Application

• Current Assignee: eBay Inc.
• Original Assignee: eBay Inc.
• Inventors: Moghtaderi, Azadeh
• Application Number:
  US14/542,772
• Publication Number:
  US 20160140584A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

A data prediction method to apply to a time series. In some embodiments, the data may be decomposed into a superposition of two or more components, which each represent different facets of the data. In further embodiments presented herein, the data may be decomposed into components representing: slowly-varying oscillations; cyclical and known instantaneous (non-stationary) disturbances; and background stationary noise effects. Each component may then be subjected to its own prediction algorithm. The predicted values of each component may then be composed to obtain a final prediction of the original data.

Claims

1. A computer-implemented method comprising:

obtaining a time series, comprising real-world data over a specified period of time;

decomposing the time series into a superposition of a plurality of components;

for each one of the plurality of components, selecting a corresponding prediction algorithm;

generating a corresponding model for each of the plurality of components and the slowly varying oscillations;

extrapolating each of the corresponding models for each of the plurality of components in order to obtain a component prediction;

combining the component predictions of each of the plurality of components with the slowly varying oscillation component to generate a final prediction on the time series, representing predicted behavior of the online marketplace; and

presenting the final prediction.

2. The method of claim 1, wherein the plurality of components comprises a slowly varying oscillation component, a cyclical and known instantaneous disturbances component, and a stationary background noise component;

wherein the slowly varying oscillations components is automatically separated from the time series first, leaving a residual; and

the cyclical and known instantaneous disturbances component are identified and separated from the residual, leaving the stationary background noise.

3. The method of claim 2, wherein the slowly varying oscillations may be data associated with trends and seasonality.

4. The method of claim 2, wherein the cyclical and known instantaneous disturbances may be data associated with cycles, holiday effects, promotional effects, or the like.

5. The method of claim 2, wherein the separation of the slowly varying oscillation component is done automatically, and without manual tuning by an operator.

6. The method of claim 1, wherein the corresponding model for each of the plurality of components is generated based on the corresponding prediction algorithm.

7. The method of claim 2, wherein the corresponding model for the slowly varying oscillations is generated using empirical mode decomposition.

8. The method of claim 2, wherein the corresponding model for the cyclical and known instantaneous disturbances is obtained using Multiple Linear Regression.

9. The method of claim 2, wherein the component prediction for the stationary background noise is obtained with an autoregressive model having coefficients;

wherein the coefficients are estimated using frequency-domain techniques.

10. The method of claim 8, wherein features taken into consideration to model the cyclical and known instantaneous disturbances using the Multiple Linear Regression include:

a control for data associated with days of the week;

holiday data based on their type and attributes; and

promotional and marketing data.

11. The method of claim 1, wherein the final prediction corresponding to the time series is used for the purpose of anomaly detection.

12. The method of claim 1, wherein the final prediction is obtained through calculating a sum of the component predictions of each of the plurality of components of the time series.

13. A system for making a prediction based on a time series, comprising:

a machine having a memory and at least one processor; and

at least one module, executable by the at least one processor, comprising;

a source module, configured to obtain the time series;

a decomposition module, configured to decompose the time series into a superposition of a plurality of components;

an algorithm selection module, configured to select an appropriate prediction algorithm to apply to each of the plurality of components;

a modeling module, configured to model each of the components separately;

a prediction module, configured to extrapolate each model in order to obtain a component prediction;

a model summation module, configured to obtain a final prediction for the time series; and

a presentation module, configured to present the final prediction.

14. The system of claim 13, wherein the decomposition module may decompose the time series into a component associated with slowly varying oscillations, a component associated with cyclical and known instantaneous disturbances, and a component associated with a stationary background noise.

15. The system of claim 13, wherein the algorithm selection module may select the appropriate prediction algorithm for each of the plurality of components, based on one or more parameters of each of the plurality of components.

16. The system of claim 13, wherein the model generated by the modeling module is based on the corresponding prediction algorithm of each of the plurality of components.

17. The system of claim 13, wherein the final prediction is obtained through calculating a sum of the component predictions of each of the plurality of components of the time series.

18. A non-transitory machine-readable storage medium storing a set of instruction that, when executed by at least one processor, causes the at least one processor to perform a set of operations comprising:

obtaining a time series;

decomposing the residual into a superposition of a plurality of components;

selecting a corresponding prediction algorithm to apply to each of the plurality of components based on one or more parameters of the corresponding component;

generating a corresponding model for each of the plurality of components and the slowly varying oscillation component, based on the corresponding prediction algorithm;

extrapolating each of the corresponding models for each of the plurality of components in order to obtain a component prediction;

combining the component predictions for each of the plurality of components and the slowly varying oscillation component to create a final prediction for the time series; and

presenting the final prediction.

19. The non-transitory machine-readable storage medium of claim 18, the superposition of a plurality of components comprises a slowly varying oscillation component, a cyclical and known instantaneous disturbances component, and a stationary background noise component;

wherein the slowly varying oscillations components is separated from the time series first, leaving a residual; and

the cyclical and known instantaneous disturbances component are identified and separated from the residual, leaving the stationary background noise.

20. The non-transitory machine-readable storage medium of claim 18, storing a set of instruction that, when executed by at least one processor, causes the at least one processor to decompose the time series into the plurality of components automatically and without manual tuning and operator oversight.