Flow rate prediction device, mixing ratio estimation device, method, and computer-readable recording medium

• US 10,445,444 B2
• Filed: 07/29/2015
• Issued: 10/15/2019
• Est. Priority Date: 08/01/2014
• Status: Active Grant

• Current Assignee: NEC Corporation
• Original Assignee: NEC Corporation
• Inventors: Yoshida, Hiroshi
• Primary Examiner(s): Shah, Kamini S
• Assistant Examiner(s): Khan, Iftekhar A
• Application Number:
  US15/329,632
• Publication Number:
  US 20170220711A1
• Time in Patent Office: Days
• Field of Search: 703 9
• CPC Class Codes: G06F 2111/08 Probabilistic or stochastic CAD
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: H04L 41/142 using statistical or mathematical methods
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04L 43/0894 Packet rate

Abstract

Communication throughput or fluctuations therein is more accurately predicted. A flow rate prediction device of the present invention is provided with: a stationarity discerning means for discerning whether the state of fluctuation in a flow rate is a steady state or non-steady state, on the basis of flow rate time series data; a mixing ratio estimating means which uses the discernment result, a mixing ratio fluctuation model, and a discernment result observation model, and which estimates the mixing ratio in the flow rate in a designated duration; and a model mixing means for mixing a steady model and a non-steady model, on the basis of the estimated mixing ratio, to construct a mixed model.

Claims

1. A flow rate prediction device, comprising:

hardware, including at least one processor and memory;

a stationarity discerning unit implemented at least by the hardware and configured to discern whether a state of fluctuation in the flow rate is a steady state or a non-steady state based on flow rate time series data, the flow rate time series data being time series data of measured values of flow rate;

a mixing ratio estimating unit implemented at least by the hardware and configured to estimate a mixing ratio of a steady state to a non-steady state both being included in the state of fluctuation in the flow rate at a specified time out of times corresponding to measured values included in the flow rate time series data by using a discernment result discerned by the stationarity discerning unit, a mixing ratio fluctuation model and a discernment result observation model, the mixing ratio fluctuation model modeling probabilistic fluctuation in a mixing ratio representing a proportion of a steady state to a non-steady state both being included in the state of fluctuation in the flow rate and the discernment result observation model indicating a probability of the discernment result discerned by the stationarity discerning unit being one predetermined state with respect to any mixing ratio; and

a model mixing unit implemented at least by the hardware and configured to mix a steady model that represents fluctuation in the flow rate in a steady state and a non-steady model that represents fluctuation in the flow rate in a non-steady state to construct a mixed model that serves as a fluctuation model of the flow rate based on a mixing ratio estimated by the mixing ratio estimating unit, improving accuracy in predicting communication throughput or fluctuation, wherein

the stationarity discerning unit discerns whether the state of fluctuation in the flow rate during a predetermined period defined using a specified time as a reference out of times corresponding to measured values included in the flow rate time series data is a steady state or a non-steady state, and

the mixing ratio estimating unit, using the mixing ratio fluctuation model and the discernment result observation model that have been prepared in advance, applies a particle filter to discernment result time series data, the particle filter achieving Bayesion inference using Monte Carlo simulation, the discernment result time series data arranging discernment results from the stationarity discerning unit in the order of times used a references in discernment, to estimate a mixing ratio included in the state of fluctuation in the flow rate at a specified time.

2. The flow rate prediction device according to claim 1, further comprising:

a flow rate predicting unit implemented at least by the hardware and configured to calculate a value of the flow rate at a time after a latest time corresponding to a measured value included in the flow rate time series data or stochastic diffusion that is stochastic expansion of the flow rate from the latest time to any given time using the mixed model constructed by the model mixing means.

3. The flow rate prediction device according to claim 1, wherein

the stationarity discerning unit discerns whether the state of fluctuation in the flow rate is a steady state or a non-steady state using a unit root test.

4. The flow rate prediction device according to claim 1, wherein

the mixing ratio fluctuation model is represented by a probability distribution function that has a truncated distribution that is obtained by truncating a normal distribution with a mixing ratio at a previous time set to a mean value to a domain of a mixing ratio.

5. A flow rate prediction method, the method comprising:

discerning whether a state of fluctuation in the flow rate is a steady state or a non-steady state based on flow rate time series data ,the flow rate time series data being time series data of measured values of flow rate;

estimating a mixing ratio of a steady state to a non-steady state both being included in the state of fluctuation in the flow rate at a specified time out of times corresponding to measured values included in the flow rate time series data by using the result of discernment, a mixing ratio fluctuation model and a discernment result observation model, the mixing ratio fluctuation model modeling probabilistic fluctuation in a mixing ratio representing a proportion of a steady state to a non-steady state both being included in the state of fluctuation in the flow rate and the discernment result observation model indicating a probability of a discernment result of the stationarity being one predetermined state with respect to any mixing ratio; and

mixing a steady model that represents fluctuation in the flow rate in a steady state and a non-steady model that represents fluctuation in the flow rate in a non-steady state to construct a mixed model that serves as a fluctuation model of the flow rate based on the mixing ratio being estimated, improving accuracy in predicting communication throughput or fluctuation, wherein

discerning whether the state of fluctuation is the steady state or the non-steady state discerns whether the state of fluctuation in the flow rate during a predetermined period defined using a specified time as a reference out of times corresponding to measured values included in the flow rate time series data is a steady state or a non-steady state, and

estimating the mixing ratio applies, using the mixing ratio fluctuation model and the discernment result observation model that have been prepared in advance, a particle filter to discernment result time series data, the particle filter achieving Bayesian inference using Monte Carlo simulation, the discernment result time series data arranging discernment results from the stationarity discerning unit in the order of times used a references in discernment, to estimate a mixing ratio included in the state of fluctuation in the flow rate at a specified time.

6. A non-transitory computer-readable recording medium storing a flow rate prediction program, the program making a computer execute:

a process of discerning whether a state of fluctuation in the flow rate is a steady state or a non-steady state based on flow rate time series data, the flow rate time series data being time series data of measured values of flow rate;

a process of estimating a mixing ratio of a steady state to a non-steady state both being included in the state of fluctuation in the flow rate at a specified time out of times corresponding to measured values included in the flow rate time series data by using the discernment result, a mixing ratio fluctuation model and a discernment result observation model, the mixing ratio fluctuation model modeling probabilistic fluctuation in a mixing ratio representing a proportion of a steady state to a non-steady state both being included in the state of fluctuation in the flow rate, the discernment result observation model indicating a probability of a discernment result of the stationarity being one predetermined state with respect to any mixing ratio; and

a process of mixing a steady model that represents fluctuation in the flow rate in a steady state and a non-steady model that represents fluctuation in the flow rate in a non-steady state to construct a mixed model that serves as a fluctuation model of the flow rate based on the estimated mixing ratio, improving accuracy in predicting communication throughput or fluctuation, wherein

the process of discerning discerns whether the state of fluctuation in the flow rate during a predetermined period defined using a specified time as a reference out of times corresponding to measured values included in the flow rate time series data is a steady state or a non-steady state, and

the the processing of estimating applies, using the mixing ratio fluctuation model and the discernment result observation model that have been prepared in advance, a particle filter to discernment result time series data, the particle filter achieving Bayesian inference using Monte Carlo simulation, the discernment result time series data arranging discernment results from the stationarity discerning unit in the order of times used a references in discernment, to estimate a mixing ratio included in the state of fluctuation in the flow rate at a specified time.