Combined prediction method based on double-cycle Holt-Winters model and SARIMA (Spatial ARIMA Model Architecture) model

Abstract

The invention discloses a combined prediction method based on a double-period Holt-Winters model and a SARIMA model, and belongs to the field of wireless network flow prediction and network optimization. The method comprises the following steps: firstly, extracting m pieces of wireless network flow data of a base station, carrying out double-period Holt-Winters model prediction and SARIMA model prediction with a prediction step length of k by utilizing first m-k items of data, respectively storing the data into an array, respectively comparing original flow data at m-k-m moments with prediction result sums, and calculating error quadratic sums EDHW and ESA; determining weight coefficients of the DHW model and the SARIMA model by using an error quadratic sum reciprocal method; and similarly, carrying out double-cycle Holt-Winters model prediction and SARIMA model prediction on the first m items of data, storing prediction results at m + 1 to m + k moments into arrays yDHW and ySA, and carrying out weighted combination on the prediction results at the corresponding moments by utilizing weight coefficients wDHW and wSA to obtain prediction results ycombine [i] of subsequent k time granularities. And finally, the change condition of the flow data in the next k hours is observed by utilizing a prediction result. The method is high in calculation efficiency, and improves the stability and accuracy.

Description

technical field [0001] The invention belongs to the field of wireless network traffic forecasting and network optimization, in particular to a combined forecasting method based on a double-period Holt-Winters model and a SARIMA model. Background technique [0002] With the rapid development of mobile communication technology, people's demand for communication is gradually increasing, and with the continuous popularization of mobile devices, wireless networks have also quietly entered people's production and life, and gradually become an indispensable tool. Explosive growth of wireless network traffic. [0003] The coming 5G era and high-speed and low-latency transmission have enriched the application scenarios of wireless networks, resulting in the continuous growth of wireless network traffic data, the network is becoming more and more congested, and users' requirements for quality of experience are also increasing. The higher it is, the more difficult it is for network op...

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Problems solved by technology

[0005] In order to solve the problem that the stability of the prediction accuracy of the single forecast model is not high, the present invention proposes to change the Holt-Winters model into the double-period Holt-Winters model (DHW), and the forecast result of DHW and the seasonal difference autoregressive The prediction results of the moving average (SARIMA) model are linearly weighted and combined to obtain the final prediction result

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