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Segmental online support vector regression method applied in traffic prediction

A support vector regression and traffic volume technology, which is applied in wireless communication, instruments, computing models, etc., can solve problems such as limited data history knowledge or models, decreased algorithm running speed, and decreased algorithm generalization ability, so as to avoid incremental The effect of the decline of online learning efficiency, the improvement of algorithm efficiency, the improvement of execution efficiency and prediction accuracy

Inactive Publication Date: 2009-11-18
HARBIN INST OF TECH
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Problems solved by technology

[0048] In the process of using the Online SVR algorithm for time series forecasting, the efficiency of the algorithm and the accuracy of the forecast are in conflict, and the continuous increase in the sample size leads to an increase in the amount of calculation involved in each online model update process, resulting in a decrease in the running speed of the algorithm ; and if less modeling is used to predict the length of the data, the historical knowledge or patterns of the data that the OnlineSVR model can record are too limited, resulting in a decrease in the generalization ability of the algorithm, resulting in a decrease in prediction accuracy

Method used

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  • Segmental online support vector regression method applied in traffic prediction
  • Segmental online support vector regression method applied in traffic prediction
  • Segmental online support vector regression method applied in traffic prediction

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specific Embodiment approach 1

[0070] A piecewise online support vector regression method applied to traffic time series forecasting:

[0071] Definition: Online support vector regression model Online SVR, penalty parameter C, insensitive loss coefficient ε, kernel function type KernelType, kernel function parameter p, sub-segmentation model SOSVR(s), s=1, 2, 3..., Initial training set length TrainLength, segmentation condition SGP, selection of optimal sub-segmentation model prediction output condition SBPP, segmentation update mechanism UpdateSOSVR(l), l∈(1, 2, ..., s), embedding dimension EmbededDimension ;

[0072] Output: i-th step forecast value PredictL(i), time series real value Test(i);

[0073] Such as image 3 and Figure 4 Shown, method of the present invention comprises the steps:

[0074] Step 1. Data preprocessing: transform the time series data and perform phase space reconstruction to make it conform to the set embedded dimension EmbededDimension;

[0075] Step 2, Online SVR initializa...

specific Embodiment approach 2

[0088] Specific embodiment 2: In this embodiment, the segmentation condition SGP described in step 4 is used to ensure that the segmentation condition can save the historical knowledge of the data set with maximum efficiency, so that the difference of each sub-segment SOSVR model is maximized, and the model is enhanced. Generalization. Other steps are the same as in the first embodiment.

specific Embodiment approach 3

[0089] Embodiment 3: In step 4 of this embodiment, a clustering method is used as a segmentation condition, so that each sub-segment SOSVR(s) adapts to sub-time series segments with different characteristics. Other steps are the same as those in Embodiment 1 or 2.

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Abstract

The invention relates to an online support vector regression (SVR) method, in particular to a segmental online SVR method applied in traffic prediction. For the online SVR algorithm is difficult to realize both the prediction accuracy and operation efficiency, the invention provides a method for predicting segmental online SVR time series. The method comprises the following steps: realizing rapid training by reducing the data length of the online modeling; carrying out the segmental storage on the online SVR; selecting and outputting the optimal sub-segmental model prediction according to the predicted matching degree between the neighborhood samples and each sub-segmental SVR model, thereby improving the prediction accuracy. Compared with the common online SVR algorithm, the algorithm of the invention can improve the prediction accuracy by 5% to 10% while maintaining the execution efficiency of the online prediction; by adopting the segmental strategy and shorter modeling data length, the efficiency of the algorithm is high; the invention is capable of online, real-time and rapid modeling and predicting the time series of mobile communication traffic.

Description

technical field [0001] The invention relates to an online support vector regression method, in particular to a segmented online support vector regression method applied to mobile communication traffic time series prediction. Background technique [0002] With the continuous development of computer science and technology, the acquisition of knowledge and data has become easier and has exploded. Effective mining of known data, extraction of data features, and prediction of future states through known sequences make data mining a At the same time, forecasting technology has become the key content of time series data mining, attracting more and more researchers' attention. [0003] The size of the mobile communication traffic reflects the strength of the voice channel being occupied to a certain extent. Mobile communication traffic data prediction is of great value for mobile network maintenance and mobile communication decision-making. If the traffic network traffic can be pre...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W24/06G06N1/00G06N99/00
Inventor 彭宇乔立岩刘大同彭喜元王建民
Owner HARBIN INST OF TECH
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