FLOW RATE PREDICTION DEVICE, FLOW RATE PREDICTION METHOD, AND FLOW RATE PREDICTION PROGRAM

• US 20150180740A1
• Filed: 06/28/2013
• Published: 06/25/2015
• Est. Priority Date: 07/02/2012
• Status: Active Grant

• Current Assignee: NEC Corporation
• Original Assignee: NEC Corporation
• Inventors: Yoshida, Hiroshi
• Granted Patent: US 9,722,892 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: H04L 41/142 using statistical or mathematical methods
• CPC Class Codes: H04L 43/04 Processing captured monitoring data, e.g. for logfile generation
• CPC Class Codes: H04L 43/0894 Packet rate
• CPC Class Codes: H04L 47/83 based on usage prediction
• CPC Class Codes: H04W 24/08 Testing, supervising or monitoring using real traffic

Abstract

A flow rate prediction device to predict a flow rate determines, based on time-series data of a measured flow rate, whether the state of the flow rate is stationary or non-stationary. The flow rate prediction device identifies a stochastic process model defining the flow rate as a stochastic variable based on the result of determination of whether the state is stationary or non-stationary and calculates a parameter for use in the identified stochastic process model based on the time-series data. The flow rate prediction device calculates a probability distribution function or a probability density function defining the flow rate to be predicted as the stochastic variable based on the identified stochastic process model and the parameter.

Claims

1. A device comprising:

a determining unit configured to determine, based on time-series data of a measured flow rate, whether a state of the flow rate is stationary or non-stationary;

a identifying unit configured to identify a stochastic process model defining the flow rate as a stochastic variable based on a result of determination made by the determining unit and calculates a parameter for use in the identified stochastic process model based on the time-series data; and

a calculating unit configured to calculate a probability distribution function or a probability density function defining the flow rate to be predicted as the stochastic variable based on the stochastic process model identified by the identifying unit and the parameter calculated by the identifying unit.

2. The device according to claim 1, further comprising a measuring unit is configured to measure the time-series data of the flow rate.

3. The device according to claim 1, wherein the calculating unit further calculates stochastic diffusion corresponding to stochastic spread of the flow rate to be predicted based on the probability distribution function or the probability density function.

4. The device according to claim 1, wherein the determining unit performs a unit root test for the determination of whether the state of the measured flow rate is stationary or non-stationary.

5. The device according to claim 1, wherein

the stochastic process model identified by the identifying unit is a non-stationary stochastic process model when the unit determines that the state of the measured flow rate is non-stationary, and

the stochastic process model identified by the identifying unit is a stationary stochastic process model different from the non-stationary stochastic process model when the determining unit determines that the state of the measured flow rate is stationary.

6. The device according to claim 1, wherein the stochastic process model identified by the identifying unit is an autoregression (AR) model.

7. A method comprising:

determining, based on time-series data of a measured flow rate, whether a state of the flow rate is stationary or non-stationary;

identifying a stochastic process model defining the flow rate as a stochastic variable based on a result of the determination;

calculating a parameter for use in the identified stochastic process model based on the time-series data; and

calculating a probability distribution function or a probability density function defining the flow rate to be predicted as the stochastic variable based on the identified stochastic process model and the calculated parameter.

8. A non-transitory computer readable medium embodying instructions for controlling a device to implement a method, the method comprising:

determining, based on time-series data of a measured flow rate, whether a state of the flow rate is stationary or non-stationary;

identifying a stochastic process model defining the flow rate as a stochastic variable based on a result of the determination;

calculating a parameter for use in the identified stochastic process model based on the time-series data; and

calculating a probability distribution function or a probability density function defining the flow rate to be predicted as the stochastic variable based on the identified stochastic process model and the calculated parameter.