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Online time sequence prediction method and system based on granularity intuitionistic fuzzy cognitive map

A fuzzy intuition, time series technology, applied in relational databases, database models, character and pattern recognition, etc., can solve the problems of inaccurate prediction, complex structure, incomplete results, etc., to improve prediction accuracy, model prediction accuracy, avoid The effect of subjective influence

Inactive Publication Date: 2020-03-10
SHANDONG NORMAL UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] The inventors of the present disclosure found that (1) the time series has the characteristics of high dimensionality, large quality, and complex structure. The previous intuitive fuzzy cognitive map was constructed based on the experience and known knowledge of experts, which could not satisfy various requirements. (2) The existing direct fuzzy cognitive map is predicted by establishing a numerical weight matrix, and the results covered during the prediction are not comprehensive, resulting in inaccurate final predictions

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  • Online time sequence prediction method and system based on granularity intuitionistic fuzzy cognitive map
  • Online time sequence prediction method and system based on granularity intuitionistic fuzzy cognitive map
  • Online time sequence prediction method and system based on granularity intuitionistic fuzzy cognitive map

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Embodiment 1

[0084] like Figure 1-19 As shown, Embodiment 1 of the present disclosure provides an online time series prediction method based on granular intuitionistic fuzzy cognitive graph, and the steps are as follows:

[0085] Preprocess the acquired time series raw data, and convert the one-dimensional time series into the form of two-dimensional space;

[0086] Using fuzzy C-means clustering algorithm to cluster the preprocessed time series data into model nodes, using particle swarm optimization algorithm to train and optimize the numerical weight matrix and granularity parameters of the intuitionistic fuzzy cognitive graph;

[0087] Using the optimized granularity parameters, the numerical weight matrix of the intuitionistic fuzzy cognitive graph model is expanded into a granularity weight matrix, and a granular intuitionistic fuzzy cognitive graph prediction model is constructed;

[0088] Input the time series data to be predicted into the trained granular intuition fuzzy cogniti...

Embodiment 2

[0225] Embodiment 2 of the present disclosure provides an online time series prediction system based on a granular intuitive fuzzy cognitive graph, including:

[0226] The preprocessing module is configured to: preprocess the acquired time series raw data, and convert the one-dimensional time series into the form of two-dimensional space;

[0227] The parameter training and optimization module is configured to: use the fuzzy C-means clustering algorithm to cluster the preprocessed time series data into model nodes, and use the particle swarm optimization algorithm to calculate the numerical weight matrix and granularity parameters of the intuitionistic fuzzy cognitive graph. training and optimization;

[0228] The granular intuitionistic fuzzy cognitive graph prediction model building module is configured to: use the optimized granularity parameters to expand the numerical weight matrix of the intuitionistic fuzzy cognitive graph model into a granularity weight matrix to const...

Embodiment 3

[0231] Embodiment 3 of the present disclosure provides a readable storage medium on which a program is stored, and when the program is executed by a processor, implements the online time series prediction method based on granular intuitionistic fuzzy cognitive graph as described in the first aspect of the present disclosure steps in .

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Abstract

The invention provides an online time sequence prediction method and system based on a granularity intuitionistic fuzzy cognitive map. The method comprises the following steps: converting a one-dimensional time sequence into a two-dimensional space form; clustering the preprocessed time series data into model nodes by using a fuzzy C-means clustering algorithm, and training and optimizing a numerical value weight matrix and granularity parameters of an intuitionistic fuzzy cognitive map by using a particle swarm optimization algorithm; expanding the numerical value weight matrix of the intuitionistic fuzzy cognitive map model into a granularity weight matrix by utilizing the optimized granularity parameters, and constructing a granularity intuitionistic fuzzy cognitive map prediction model; inputting to-be-predicted time sequence data into the trained granularity intuitionistic fuzzy cognitive map prediction model, and reasoning to obtain data at the next moment. According to the method and the device, the limitation of human factors is avoided, model establishment from original sequence data can be achieved, errors of a modeling process are considered, the prediction precision isimproved, results are expanded into intervals, and the results are covered as many as possible.

Description

technical field [0001] The present disclosure relates to the technical field of time series forecasting, in particular to an online time series forecasting method and system based on a granular intuitionistic fuzzy cognitive map. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] Time series refers to a series of events that occur sequentially in time. Observations at different times cannot be considered independent of each other. To be precise, there are certain correlation patterns between these continuous observations. Starting from the research of basic information characteristics, the time series forecasting model is established, and the development trend of the time series is predicted. Time series modeling is one of the main interests of mathematics and information science. This is largely due to the ubiquity of real-world phenomen...

Claims

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Application Information

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IPC IPC(8): G06F16/2458G06F16/28G06K9/62
CPCG06F16/2468G06F16/2474G06F16/288G06F16/285G06F18/214
Inventor 骆超张楠楠
Owner SHANDONG NORMAL UNIV
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