Data processing: generic control systems or specific application – Generic control system – apparatus or process – Optimization or adaptive control
Reexamination Certificate
2000-01-27
2004-05-04
Follansbee, John A. (Department: 2121)
Data processing: generic control systems or specific application
Generic control system, apparatus or process
Optimization or adaptive control
C706S021000, C702S189000
Reexamination Certificate
active
06731990
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates to a computer system and method for predicting a future value of a series of data and in particular, but in no way limited to, a computer system and method for predicting a future value of a series of communications data or product data.
2. Description of the Prior Art
Predicting future values of a series of data is a difficult problem that is faced by managers and operators of processes and systems such as communications networks or manufacturing processes. For example, a communications network has a limited capacity and traffic levels within that network need to be managed effectively to make good use of the available resources whilst minimising congestion. However, previously, methods of predicting future values of data series such as communications data or product data have not been accurate enough to be used effectively. Another problem is that such methods of predicting are often computationally expensive and time consuming such that predicted values are not available far enough in advance to be useful.
Network and service providers typically enter into contracts with customers in which specified quality of service levels and other metrics are defined. Penalty payments are incurred in the event that these agreed service levels and metrics are not met and this is another reason why predicting future values of data series, such as communications data is important. By using such predicted values better management of communications network resources could be made such that contractual agreements are met.
Previously the approach of statistical process control (SPC) has been used to analyse data series. Data samples were obtained, such as traffic levels in a communications network at a particular time and data from these samples would then be used to make inferences about the whole population of traffic level data over time for the communications network. Typically, statistics such as the mean and standard deviation or range were calculated for the sample data for each parameter, and these statistics compared for different samples. For example, if the mean was observed to move outside a certain threshold range an “out of control” flag would be triggered to alert the network operators to a problem in the communications network. If trends were observed in the data, for example, an increase in the mean, the operator could be alerted to this fact and then an investigation carried out.
Several problems with these statistical approaches are known. For example, an inference is made that the data sets fit a standard type of distribution, such as a normal or Poisson distribution. However, this is rarely the case for communications network data in which many outlying values are typically observed and which are often bimodal or show other irregular distributions. Also, data may be obtained from a small sample of the actual data series and used to make inferences about the whole population of data. This means that the statistics calculated are often not an accurate reflection of the process being analysed.
Another problem is that data that is available is often not suitable for statistical analysis. This is because the data sets are often small, incomplete, discontinuous and because they contain outlying values. However, this type of data is typically all that is available for communications network management, process control or other purposes.
The problems mentioned above also apply to process control and to data series of product data. Another problem in process control is being able to deal with the fact that the inputs to the process vary. For example, if components are supplied to a manufacturer for assembly into a final product, those components may vary from batch to batch and from supplier to supplier. However, it is very difficult to analyse how the components vary and this is time consuming and expensive. Also, it is difficult to determine what effect variations in the components may have on the manufacturing process that is being controlled. These problems increase for more complex products that involve many components, such as circuit boards. For this reason, many manufacturers aim to limit variability by attempting to strictly control all the initial build conditions which includes the supply base. This is often not possible if it is necessary to vary the supplier for other reasons, for example to attain a good price or to achieve continuity of supply. Many manufacturers of electronic systems rely heavily upon their suppliers to ensure that materials and components used in the fabrication of products are compliant to specification. Often, electronic components are not examined before they enter factories. Investment programmes for test equipment at the component level have shown that it is not practical to distinguish between batches of components and also that the instances of non-compliant components are negligible. For these reasons many manufacturing companies have wound down their incoming component inspection processes. Instances do occur where manufactured products exhibit changes in performance that are attributed to changes in the components but no effective way of dealing with this problem has been found.
A particular problem in process control involves the situation where a manufacturing process is set up in a particular location, such as the USA, and it is required to set up the same process in a new location, say Canada, in order to produce the same quality of product with the same efficiency. It is typically very difficult to set up the new process in such a way that the same quality of product is produced with the same efficiency because of the number of factors that influence the process.
Failure mode effect analysis is another problem in management of communications networks, communications equipment, or in process control. In this case, a failure occurs in the process and it is required to analyse why this has occurred and what corrective action should be taken. Current methods for dealing with failure mode effect analysis include schematic examination and fault injection techniques but these are not satisfactory because of the problems with the data mentioned above.
JP8314530 describes a failure prediction apparatus which uses chaos theory based methods. A physical quantity, such as an electrical signal, showing the condition of a single installation is measured repeatedly at regular intervals in order to collect a time series of data. This time series of data is then used to reconfigure an attractor which is used to predict future values of the time series. These predicted values are compared with observed values in order to predict failure of the installation. This system is disadvantageous in many respects. The input data must be repeated measurements from a single apparatus taken at regular intervals. However, in practice it is often not possible to obtain measurements at regular intervals. Also, JP8314530 does not address the problems of dealing with communications data, product data and non time series data such as product data obtained from many products which will vary. Also, JP8314530 is concerned with failure prediction only and not with other matters such as monitoring performance and detecting changes in behaviour of a process. Moreover, JP8314530 does not describe the process of identifying nearest neighbour vectors and determining corresponding vectors for these.
It is accordingly an object of the present invention to provide a computer system and method for predicting a future value of a series of data which overcomes or at least mitigates one or more of the problems noted above.
SUMMARY OF THE INVENTION
According to a first aspect of the present invention there is provided a method of predicting a future value of a series of data comprising the steps of:
(i) forming a set of vectors wherein each vector comprises a number of successive values of the series of data;
(ii) identifying from said set of vectors, a current vector which comprises a most recent value of th
Carter Malcolm Edward
Dodson Michael Maurice
Fojt Otakar
Hobbs Christopher
Levesley Jason
Barnes Crystal J
Barnes & Thornburg
Follansbee John A.
Nortel Networks Limited
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