Research on the application of Prophet model in intelligent prediction of call volume

CHEN Yong; LIU Yifeng; WANG Yin; ZHANG Mengyu

doi:10.11959/j.issn.1000-0801.2024235

您当前的位置：

首页 >

文章列表页 >

Research on the application of Prophet model in intelligent prediction of call volume

Engineering and Application | 更新时间：2025-03-17

- Research on the application of Prophet model in intelligent prediction of call volume
- Telecommunications Science Vol. 40, Issue 11, Pages: 160-169(2024)
- 作者机构：
  
  中移信息技术有限公司，广东深圳 518048
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2024235
  CLC： TN929.5
- Received：12 July 2024，
  
  Revised：21 October 2024，
  
  Published：20 November 2024
- 稿件说明：
移动端阅览
陈勇,刘贻凤,王茵等.Prophet模型在话单量智能预测中的应用研究[J].电信科学,2024,40(11):160-169.

CHEN Yong,LIU Yifeng,WANG Yin,et al.Research on the application of Prophet model in intelligent prediction of call volume[J].Telecommunications Science,2024,40(11):160-169.
陈勇,刘贻凤,王茵等.Prophet模型在话单量智能预测中的应用研究[J].电信科学,2024,40(11):160-169. DOI： 10.11959/j.issn.1000-0801.2024235.

CHEN Yong,LIU Yifeng,WANG Yin,et al.Research on the application of Prophet model in intelligent prediction of call volume[J].Telecommunications Science,2024,40(11):160-169. DOI： 10.11959/j.issn.1000-0801.2024235.

摘要

对于电信运营商而言，话单量预测在业务运营、IT系统建设规划以及系统运维方面都是至关重要的一环。传统的预测方法采用以人工预测为主，程序脚本统计为辅的方式来完成，预测的准确性受人为因素影响较大，且固定的规则无法表征受到多重因素影响的话单量的变化规律，因此，需要引入人工智能模型进行话单量的动态预测。通过研究并测试常用的人工智能预测模型，采用依据业务特征改进的Prophet模型对网间结算系统的话单量进行预测，预测效果相较于传统预测方法获得了显著提升。

Abstract

For telecommunications operators

call volume prediction is crucial in business operations

IT system construction planning

and system operation and maintenance. The traditional prediction method is mainly achieved through manual prediction

supplemented by program script statistics. The accuracy of predictions is greatly influenced by human factors

and fixed rules cannot represent the variation pattern of call volume affected by multiple factors. Therefore

it is necessary to introduce artificial intelligence models for dynamic prediction of call volume. Commonly used artificial intelligence prediction models were studied and tested. The Prophet model

improved based on business characteristics

was adopted for forecasting call volume of the inter-network settlement systems. The prediction effect has been significantly improved compared to traditional prediction.

关键词

Keywords

references

赵珂一 . 基于ARIMA模型的云南省第三产业发展预测 [J ] . 价值工程 , 2023 , 42 ( 33 ): 44 - 48 .

ZHAO K Y . Forecasting the service industry development in Yunnan Province based on ARIMA model [J ] . Value Engineering , 2023 , 42 ( 33 ): 44 - 48 .

王晓 , 揣锦华 , 张立恒 . 基于Prophet算法的铁路客流量预测研究 [J ] . 计算机技术与发展 , 2020 , 30 ( 6 ): 130 - 134, 150 .

WANG X , CHUAI J H , ZHANG L H . Research on railway passenger flow forecast based on Prophet time series algorithm [J ] . Computer Technology and Development , 2020 , 30 ( 6 ): 130 - 134, 150 .

钱嫣然 , 何慧之 , 仇海英 , 等 . 基于ARIMA模型的区域用电量预测方法 [J ] . 信息技术 , 2023 , 47 ( 1 ): 180 - 185 .

QIAN Y R , HE H Z , QIU H Y , et al . Regional electricity consumption forecasting method based on ARIMA model [J ] . Information Technology , 2023 , 47 ( 1 ): 180 - 185 .

郭斌 , 熊显名 . 基于SARIMA-LSTM的区域用电消耗预测研究 [J ] . 工业控制计算机 , 2024 , 37 ( 5 ): 109 - 111, 114 .

GUO B , XIONG X M . Research on regional electricity consumption forecasting based on SARIMA-LSTM [J ] . Industrial Control Computer , 2024 , 37 ( 5 ): 109 - 111, 114 .

曹还君 , 李长云 . 基于SSA-LSTM模型的空气质量预测研究 [J ] . 现代信息科技 , 2024 , 8 ( 4 ): 142 - 146, 152 .

CAO H J , LI C Y . Research on air quality prediction based on SSA-LSTM model [J ] . Modern Information Technology , 2024 , 8 ( 4 ): 142 - 146, 152 .

史业照 , 郭斌 , 郑永军 . 基于LSTM网络的IGBT寿命预测方法研究 [J ] . 中国测试 , 2024 , 50 ( 2 ): 54 - 58, 65 .

SHI Y Z , GUO B , ZHENG Y J . Research on IGBT life prediction based on LSTM network [J ] . China Measurement & Test , 2024 , 50 ( 2 ): 54 - 58, 65 .

朱宝 , 罗孝文 , 吴自银 . 基于ARIMA与LSTM的海岸带地面沉降预测方法: 以杭州湾地区为例 [J ] . 海洋学研究 , 2022 , 40 ( 2 ): 53 - 61 .

ZHU B , LUO X W , WU Z Y . ARIMA- and LSTM-based forecasting method of land subsidence in coastal zone: a case study from the Hangzhou Bay and its adjacent area [J ] . Journal of Marine Sciences , 2022 , 40 ( 2 ): 53 - 61 .

周源 , 魏国富 , 李亚锦 , 等 . 基于LSTM的换流站直流测量系统状态趋势预测方法 [J ] . 电气自动化 , 2022 , 44 ( 4 ): 67 - 70 .

ZHOU Y , WEI G F , LI Y J , et al . State trend prediction method of DC measurement systemof converter station based on long short-term memory [J ] . Electrical Automation , 2022 , 44 ( 4 ): 67 - 70 .

谢辉 , 江雄 , 王武 . 基于LSTM的电网区域线损预测方法研究 [J ] . 电气自动化 , 2023 , 45 ( 1 ): 47 - 49 .

XIE H , JIANG X , WANG W . Research on LSTM-based regional line loss prediction method of power grid [J ] . Electrical Automation , 2023 , 45 ( 1 ): 47 - 49 .

TAYLOR S J , LETHAM B . Forecasting at Scale [J ] . The American Statistician , 2018 , 72 ( 1 ): 37 - 45 .

张新禹 . 基于ARIMA和指数平滑算法对磁盘占用率的预测分析 [J ] . 现代信息科技 , 2023 , 7 ( 24 ): 67 - 71 .

ZHANG X Y . Prediction analysis of disk utilization rate based on ARIMA and exponential smoothing algorithms [J ] . Modern Information Technology , 2023 , 7 ( 24 ): 67 - 71 .

李衡 , 朱理 , 郑洁 , 等 . 基于Prophet算法的配电网线路峰值负荷预测 [J ] . 浙江电力 , 2022 , 41 ( 3 ): 20 - 26 .

LI H , ZHU L , ZHENG J , et al . Peak load forecasting of distribution network lines based on Prophet algorithm [J ] . Zhejiang Electric Power , 2022 , 41 ( 3 ): 20 - 26 .

刘天 , 吴杨 , 陈琦 , 等 . SARIMA模型与Prophet模型对手足口病周发病率拟合及预测效果比较 [J ] . 预防医学情报杂志 , 2022 , 38 ( 10 ): 1346 - 1350 .

LIU T , WU Y , CHEN Q , et al . Comparison of SARIMA model and Prophet model on the weekly incidence of hand, foot and mouth disease [J ] . Journal of Preventive Medicine Information , 2022 , 38 ( 10 ): 1346 - 1350 .

马晓琴 , 厉娜 , 罗红郊 , 等 . 基于Prophet的用户用电量短期预测方法 [J ] . 信息技术 , 2023 , 47 ( 8 ): 29 - 34 .

MA X Q , LI N , LUO H J , et al . Prophet-based short-term forecasting method of user electricity consumption [J ] . Information Technology , 2023 , 47 ( 8 ): 29 - 34 .

刘夏雨 . 基于Prophet时间序列的5G通信网络潮汐效应预测方法 [J ] . 中国宽带 , 2023 , 19 ( 9 ): 10 - 12 .

LIU X Y . Prediction method of tidal effect in 5G communication network based on Prophet time series [J ] . China BroadBand , 2023 , 19 ( 9 ): 10 - 12 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Cloud-based intelligent computing center ten-thousand card cluster innovation and practice

Research on the evolution, key technologies, and applications for AI enabled 5G-Advanced core network running

Building the future: innovative application and development prospect of telecommunication big data open platform in for intelligent social governance

Research on fault prediction of computer network nodes driven by log information

Research on service-driven network technologies for space-air-ground-sea integration

Related Author

DING Hongqing

ZHANG Pengfei

NIU Hongweihua

LI Zhiyong

ZHOU Danyuan

DING Guoqiang

LI Panpan

LI Daotong

Related Institution

China Mobile Group Design Institute Co., Ltd.

China Mobile Communications Group Zhejiang Co., Ltd.

(Suzhou) Software Technology Co., Ltd.

China Mobile Communications Group Co., Ltd.

China Mobile Research Institute

⁰