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基于holt-winters模型的电网企业售电量预测方法

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中国电力企业管理 2018年第24期 86-87页

作者：李辉杰刘悦春国网河南省电力公司濮阳供电公司

针对电网企业售电量呈现季节性变化的特点,选择holt-winters方法建立时间序列模型,利用寻优函数得到的平滑系数确认预测精度,使用相对误差估计系统性能。实践表明该方法具有较好的预测精度,为电网运营管理提供了对应决策服务。

关键词：售电量电量预测电网企业 holt-winters

基于holt-winters模型的天然气负荷预测

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技术与市场 2020年第7期27卷 31-33页

作者：胡凯合肥燃气集团有限公司安徽合肥230001

探讨holt-winters乘法模型在企业天然气供应负荷预测中的应用价值。以2015—2018年各季度天然气负荷为基础,运用SPSS分别建立holt-winters加法模型和乘法模型,采用2019年各季度的实际数据验证模型,采用均方根误差(RMAE)作为模型评价指... 详细信息

探讨holt-winters乘法模型在企业天然气供应负荷预测中的应用价值。以2015—2018年各季度天然气负荷为基础,运用SPSS分别建立holt-winters加法模型和乘法模型,采用2019年各季度的实际数据验证模型,采用均方根误差(RMAE)作为模型评价指标。结果:RMAE的比较结果表明加法模型优于乘法模型,具有较高的预测精度,可较好的拟合企业天然气负荷的变化趋势。

关键词：天然气负荷时间序列 holt-winters 预测

基于holt-winters时间序列模型在干线公路路段流量预测中的应用

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中文科技期刊数据库（全文版）自然科学 2018年第12期 82-83页

作者：蒋正超江苏长天智远交通科技有限公司江苏南京210036

针对国省干道公路、高速公路等干线交通流量呈现趋势性、季节性变化的特征,先使用holt-winters算法构建时间序列分析模型,然后选择最优的平滑系数组合以保证预测的精度,再使用相对误差来评估建立的预测模型在车流量预测中的性能表现,最... 详细信息

针对国省干道公路、高速公路等干线交通流量呈现趋势性、季节性变化的特征,先使用holt-winters算法构建时间序列分析模型,然后选择最优的平滑系数组合以保证预测的精度,再使用相对误差来评估建立的预测模型在车流量预测中的性能表现,最后通过模型与历史数据预测某一路段未来一年内的车流量情况。

关键词： holt-winters 时间序列国省干道、高速公路流量预测模型

SutteARIMA:A Novel Method for Forecasting the Infant Mortality Rate in Indonesia

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Computers, Materials & Continua 2022年第3期70卷 6007-6022页

作者： Ansari Saleh Ahmar Eva Boj del Val M.A.El Safty Samirah AlZahrani Hamed El-Khawaga Business School Faculty of Economics and BusinessUniversitat de BarcelonaBarcelona08034Spain Department of Statistics Faculty of Mathematics and Natural ScienceUniversitas Negeri MakassarMakassar90224Indonesia Department of Economic Financial and Actuarial MathematicsFaculty of Economics and BusinessUniversitat de BarcelonaBarcelona08034Spain Department of Mathematics and Statistics Colleage of SciencesTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Statistics Mathematicsand InsuranceFaculty of CommerceTanta UniversityEgypt Department of Economics and Finance College of Business AdministrationTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

This study focuses on the novel forecasting method(SutteARIMA)and its application in predicting Infant Mortality Rate data in *** undertakes a comparison of the most popular andwidely used four forecasting methods:ARIMA,Neural Networks Time Series(NNAR),holt-winters,and *** data used were obtained from the website of the World *** data consisted of the annual infant mortality rate(per 1000 live births)from 1991 to *** determine a suitable and best method for predicting InfantMortality rate,the forecasting results of these four methods were compared based on the mean absolute percentage error(MAPE)and mean squared error(MSE).The results of the study showed that the accuracy level of SutteARIMA method(MAPE:0.83%andMSE:0.046)in predicting InfantMortality rate in Indonesia was smaller than the other three forecasting methods,specifically the ARIMA(0.2.2)with a MAPE of 1.21%and a MSE of 0.146;the NNAR with a MAPE of 7.95%and a MSE of 3.90;and the holt-winters with aMAPE of 1.03%and aMSE:of 0.083.

关键词： Forecasting infant mortality rate ARIMA NNAR holt-winters SutteARIMA

Forecasting Foreign Direct Investment to Zambia: A Time Series Analysis

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Open Journal of Statistics 2017年第1期7卷 122-131页

作者： Stanley Jere Bornwell Kasense Obvious Chilyabanyama Department of Mathematics and Statistics Mulungushi University Kabwe Zambia

Three methods are considered in this paper: Simple exponential smoothing (SES), holt-winters exponential smoothing (HWES) and autoregressive integrated moving average (ARIMA). The best fit model was then used to forecast Zambia’s annual net foreign direct investment (FDI) inflows from 1970 to 2014. Foreign direct investment is foreign capital investment to Zambia. Throughout the paper the methods are illustrated using Zambia’s annual Net FDI inflows. A comparison of the three methods shows that the ARIMA (1, 1, 5) is the best fit model because it has the minimum error. Forecasting results give a gradual increase in annual net FDI inflows of about 44.36% by 2024. Forecasting results plays a vital role to policy makers. Decision making, coming up with good policies and suitable strategic plans, depends on accurate forecasts. Zambian FDI policy makers can use the results obtained in this study and create suitable strategic plans to promote FDI.

关键词： Foreign Direct Investment Simple Exponential Smoothing holt-winters Exponential Smoothing Autoregressive Integrated Moving Average Forecasting

Oil-Price Forecasting Based on Various Univariate Time-Series Models

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American Journal of Operations Research 2016年第3期6卷 226-235页

作者： Gurudeo Anand Tularam Tareq Saeed Mathematics and Statistics Griffith Sciences (ENV) Environmental Futures Research Institute (EFRI) Griffith University Brisbane Australia Department of Mathematics Faculty of Science King Abdulaziz University Jeddah KSA

Time-series-based forecasting is essential to determine how past events affect future events. This paper compares the performance accuracy of different time-series models for oil prices. Three types of univariate models are discussed: the exponential smoothing (ES), holt-winters (HW) and autoregressive intergrade moving average (ARIMA) models. To determine the best model, six different strategies were applied as selection criteria to quantify these models’ prediction accuracies. This comparison should help policy makers and industry marketing strategists select the best forecasting method in oil market. The three models were compared by applying them to the time series of regular oil prices for West Texas Intermediate (WTI) crude. The comparison indicated that the HW model performed better than the ES model for a prediction with a confidence interval of 95%. However, the ARIMA (2, 1, 2) model yielded the best results, leading us to conclude that this sophisticated and robust model outperformed other simple yet flexible models in oil market.

关键词： Oil Price Univariate Time Series Exponential Smoothing holt-winters ARIMA Models Model Selection Criteria

纬度因素对季节性时序TEC短期预报模型的影响分析

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大地测量与地球动力学 2018年第6期38卷 581-586页

作者：陈雨田刘立龙刘中流贺朝双桂林理工大学测绘地理信息学院桂林市雁山街319号541006 广西空间信息与测绘重点实验室桂林市雁山街319号541006

通过测试选取合适的时间序列长度,分析地磁指数,排除磁暴与地磁活动的影响,针对电离层TEC值的周期性变化及其随纬度的不均匀分布规律,采用季节性时间序列模型SARIMA和指数平滑模型holtwinters,利用IGS中心提供的TEC格网数据,对北半球不... 详细信息

通过测试选取合适的时间序列长度,分析地磁指数,排除磁暴与地磁活动的影响,针对电离层TEC值的周期性变化及其随纬度的不均匀分布规律,采用季节性时间序列模型SARIMA和指数平滑模型holtwinters,利用IGS中心提供的TEC格网数据,对北半球不同纬度的48个区域进行预报,并通过定义日均相对精度和均方根误差来评定预报精度。结果表明,3种模型均能较好地反映电离层TEC值的周期性变化,但均方根误差随纬度的降低总体呈现增长趋势,且在北纬25°与55°表现为极大值,在北纬45°表现为极小值。

关键词：地磁指数电离层 TEC SARIMA holt-winters

Hadoop + Spark Platform Based on Big Data System Design of Agricultural Product Price Analysis and Prediction by holtwinters

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国际计算机前沿大会会议论文集 2019年第1期 121-123页

作者： Yun Deng Yan Zhu Qingjun Zhang Xiaohui Cheng Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin 541004 China Department of Information Science and Engineering Guilin University of Technology Guilin 541004 China

In the market of agricultural products, the price of agricultural products is affected by production cost, market supply and other factors. In order to obtain the market information of agricultural products, the price fluctuation can be analyzed and predicted. A distributed big data software platform based on Hadoop, Hive and Spark is proposed to analyze and forecast agricultural price data. Firstly, Hadoop, Hive and Spark big data frameworks were built to store the data information of agricultural products crawled into MYSQL. Secondly, the information of agricultural products crawled from MYSQL was exported to a text file, uploaded to HDFS, and mapped to spark SQL database. The data was cleaned and improved by holt-winters (three times exponential smoothing method) model to predict the price of agricultural products in the future. The data cleaned by spark SQL was imported and predicted by improved holt-winters into MYSQL database. The technologies of pringMVC, Ajax and Echarts were used to visualize the data.

关键词： Hadoop Spark Big data Analysis and forecast of agricultural product prices holt-winters

基于AHP、ARIMA算法的电力信息系统负载预测研究与应用

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电网与清洁能源 2017年第8期33卷 20-25页

作者：王旭陈潇一国网陕西省电力公司陕西西安710048 国网陕西省电力公司电力科学研究院陕西西安710100

电网公司已建成信息系统数量较多,服务器承载的信息量和访问量呈几何级数增长,在这种情况下,由于长时间累积运行可能导致系统超负荷运行或者负荷过于空闲。针对此问题,从信息系统负载角度对系统进行状态评价,首先利用电网公司的大量历... 详细信息

电网公司已建成信息系统数量较多,服务器承载的信息量和访问量呈几何级数增长,在这种情况下,由于长时间累积运行可能导致系统超负荷运行或者负荷过于空闲。针对此问题,从信息系统负载角度对系统进行状态评价,首先利用电网公司的大量历史数据指标建立指标体系,其次利用AHP方法综合CPU利用率、内存利用率、磁盘利用率3个指标得到综合资源利用率,最后利用ARIMA方法和holt-winters指数平滑方法分别对综合资源利用率和系统响应时长进行预测,根据预测结果进行分级预判系统负荷状态,评估业务系统资源是否需要进行调整,满足用户对系统的要求,并保证资源的有效利用,减少资源空闲和浪费,优化资源调度。

关键词： AHP 电力信息系统负载 ARIMA holt-winters