Prediction of Civil Aviation Passenger Transportation Based on ARIMA Model

The passenger transportation, as an important index to describe the scale of aviation passenger transport, prediction and research, can let us understand the future trend of the aviation passenger transport, according to it, the airline can make corresponding marketing strategy adjustment. Combining with the knowledge of time series let us understand the characteristics of passenger transportation change, the R software is used to fit the data, so as to establish the ARIMA(1,1,8) model to describe the civil aviation passenger transport developing trend in the future and to make reasonable predictions.

Forecast on Price of Agricultural Futures in China Based on ARIMA Model

The forecast on price of agricultural futures is studied in this paper. We use the ARIMA model to estimate the price trends of agricultural futures,which can help the investors to optimize their investing plans. The soybean future contracts are taken as an example to simulate the forecast based on the auto-regression coefficient(p),differential times(d) and moving average coefficient(q). The results show that ARIMA model is better to simulate and forecast the trend of closing prices of soybean futures contract,and it is applicable to forecasting the price of agricultural futures.

Forecasting Tesla’s Stock Price Using the ARIMA Model

The stock market is an important economic information center.The economic benefits generated by stock price prediction have attracted much attention.Although the stock market cannot be predicted accurately,the stock market’s prediction of the trend of stock prices helps in grasping the operation law of the stock market and the influence mechanism on the economy.The autoregressive integrated moving average(ARIMA)model is one of the most widely accepted and used time series forecasting models.Therefore,this paper first compares the return on investment(ROI)of Apple and Tesla,revealing that the ROI of Tesla is much greater than that of Apple,and subsequently focuses on ARIMA model’s prediction on the available time series data,thus concluding that the ARIMA model is better than the Naïve method in predicting the change in Tesla’s stock price trend.

Prediction and Analysis of Chinese Rural Households' Consumption Level Based on the ARIMA Model

By using the software SAS9.2 and the relevant data of consumption level of rural residents in China from 1952 to 2008,the ARIMA model is established.The model is used to analyze and forecast the time series of the consumption level of Chinese rural residents.The results show that in the near future,the consumption level of Chinese rural residents will be further raised.In 2012,the level will break through per capita 5 000 yuan,almost 100 times more than that in the primary time period.But consumption level does not equal to living standard.To let farmers lead a good life,the government should follow the objective laws;take the overall situation into consideration;coordinate the relations among farmers' consumption level,national subsidies and farmers' production enthusiasm.Therefore,The paper suggests that the historical and objective factors should be attached more importance to.Besides,raising farmers' income and allaying farmers' fear were effective measures in developing the consumptive potential of rural market and promoting the economic sustainable development.

Analysis and Forecast of MSW Production Based on the ARIMA Model in Beijing

Based on the data of MSW generation in Beijing from 2004 to 2012,an ARIMA model of time series analysis was established. By contrast of the modeling results of different yearly data,the forecast period was identified to be 10 years. The yearly production of MSW from 2015 to 2025 was forecasted by using SPSS 16. 0 software. Result shows that the forecasting effect of ARIMA( 1,0,1) model is relatively good,and it can be applied to prediction of MSW production in Beijing. In the next 10 years,the amount of MSW produced in Beijing is increasing,but the growth rate is not large. Is expected to 2025,the production of MSW will reach more than 9 million tons. Taking into account the MSW return,it is inferred that the production of MSW in Beijing in 2025 will be close to 10 million tons. In order to reduce the pressure of subsequent waste disposal facilities in Beijing,the government can increase the intensity of the recycling of waste materials.

Forecasting Measles Immunization Coverage Using ARIMA Model

This study aimed to find a model to forecast monthly measles immunization coverage using Autoregressive Integrated Moving Average (ARIMA). The monthly registered data for measles immunization coverage from January 2014 to December 2018 were used for the development of the model. The best model with the smallest Normalized Bayesian Information Criterion (BIC) of 8.673 is ARIMA (0, 1, 0). ARIMA (0, 1, 0) was used to forecast the monthly measles immunization coverage for the next 36 months from January 2018 to December 2020. The results obtained prove that this model can be used for forecasting future immunization coverage and will help decision-makers to establish strategies, priorities, and proper use of immunization resources.

ARIMA MODEL ON WOOD PROPERTIES VARIATION PATTERN OF KOREAN LARCH

In this paper, according to the theory and method of time-series analysis, the grow ing rings ARIMA model of wood properties variation pattern for Larix olgensis plantation was studied. The model recognition and parameter estimation were discused. The ARIMA model of wood growth ring density, growth ring widith and late wood percentage was obtained. Appling the ARIMA model which obtained from actual test fitted the variation pattem of wood growth ring for Larix olgensis. The result indicated it was an effective method that applied the ARIMA model to study wood growth ring properties variation pattem. By comparing with the actual variation pattem from test data the goodness of fit was good.

基于NAR-ARIMA组合模型的高速公路沥青路面破损状况预测

基于NAR神经网络模型和ARIMA传统时间序列预测模型,对高速公路沥青路面的破损状况进行预测,再分别通过最优加权法和残差优化法对两种预测模型进行组合,得到两种组合模型.对各单一模型和组合模型的精度和稳定性进行了比较分析.实例分析表明,组合模型相较于单一模型的精度和稳定性均有所提升,NAR-ARIMA最优加权组合模型预测效果最佳.该组合模型所需样本量较小,且基于时间序列.由于采用历史数据作为影响因素代替指标,该组合模型计算速度快、精度高,适用于日常的预测工作,为后续合理的道路养护决策提供了重要的理论依据.

基于ARIMA模型的黄土高原河谷城市生态足迹动态模拟及测算——以甘肃省兰州市为例

为了从经济发展的角度探究生态足迹动态变化的成因,论文在测算黄土高原河谷城市——甘肃省兰州市2002—2014年人均生态足迹的发展轨迹的基础上,引入ARIMA模型,模拟预测了该市2015—2020年生态足迹变化趋势。研究结果显示:1)2002—2014年,甘肃省兰州市人均生态足迹总体呈现上升态势,数值由2.70 hm^(2)增长至4.25 hm^(2),增幅达到1.57倍;2)从生态足迹增速看,2002—2014年人均生态足迹平均增速达到4.04%,同一时期兰州市地区生产总值平均增速为11.88%,较人均生态足迹增速高出7.84%,表明该地区经济发展的速度高于资源环境消耗的速度;3)2015—2020年甘肃省兰州市人均生态足迹仍然呈现上升态势,预测值分别达到4.48 hm^(2)、4.61 hm^(2)、4.75 hm^(2)、4.89 hm^(2)、5.02 hm^(2)和5.17 hm^(2),甘肃省兰州市生态赤字逐年增大,总生态足迹是城市土地利用总面积的19.59倍,说明经济发展与地区生态需求呈现较强正相关性,环境库兹涅茨曲线“拐点”并未出现,处于不可持续发展状态。基于以上分析结果提出了甘肃省兰州市降低生态足迹的具体路径:1)实施产业结构调整,降低生态赤字,提升经济发展质量和可持续发展能力;2)推动绿色发展,构建生态类型多样、布局合理、功能完善的自然生态系统和城乡一体的生态网络,提高生态环境容量。

基于小波分解和ARIMA-GARCH-GRU组合模型的制造业PMI预测

制造业采购经理人指数(PMI)是反映国家经济运行情况的重要指标,而传统预测模型对该类时序数据预测精度不高。针对制造业PMI指数的非线性、波动性和数据量少的特点,提出一种基于一维离散小波变换进行数据预处理的组合模型。时序数据经过小波变换,由整合移动平均自回归–广义自回归条件异方差模型(ARIMA-GARCH)处理稳态低频数据,门控循环单元(GRU)处理波动性强的高频数据,将各频段预测结果进行融合得到最终预测结果。为验证模型有效性,选取一定数据量的PMI指数进行实验。结果表明,与其他常见模型对比,本文构建的组合模型具有较好的预测精度与性能,平均绝对误差(MAE)、均方根误差(RMSE)、平均绝对百分比误差(MAPE)分别达到0.00329、0.004162、0.65%。

时序模型ARIMA在数据分析中的应用

时间序列是进行趋势分析的方法之一。随着大数据时代的到来,经济趋势、企业经营、市场预测和天气预测等常常需要进行预测和分析。本文对某知名化妆品公司2010年至2018年间的2122条股票数据,采用ARIMA模型进行趋势分析,预测未来的发展趋势。通过模型的拟合与效果考核,所得到的结果说明了应用ARIMA模型对股票进行趋势分析时,可以取得较好的预测效果。

融合ARIMA模型和MCMC方法的非一致性设计洪水计算

常规非一致性频率分析方法在选择协变量、建立统计参数与协变量的函数关系方面均存在主观性,且仅获得设计洪水估计值,不能同时进行不确定性分析。为改进上述不足,建立了ARIMA-MCMC模型,在贝叶斯MCMC方法抽样过程中考虑统计参数拟合期内的时变性,进而对未来气候变化条件下的非一致性设计洪水频率分布模型参数进行抽样,基于参数后验分布进行设计洪水计算,并推求相应的置信区间。选取雅砻江流域小得石水文站作为分析对象,采用ARIMA-MCMC模型定量评估未来气候变化条件下小得石站设计洪水的变化情况。结果表明:基于ARIMA-MCMC方法的参数抽样收敛效果较好,3种情景下的模型统计量D均小于显著水平5%的临界值;除SSP2-4.5情景下P=0.1%和P=0.05%的设计值外,其他情况的设计最大日流量较历史期均明显增加,其中SSP1-2.6、SSP5-8.5情景下的增幅分别为7.1%~10.5%、13.9%~27.2%。本文建立的ARIMA-MCMC方法能够有效进行非一致性设计洪水频率分析。

基于ARIMA与NNAR模型的甘肃省胰腺癌发病趋势预测

目的:分析2013-2021年甘肃省胰腺癌发病趋势,并对2022-2026年胰腺癌发病趋势进行预测。方法:收集2013-2021年甘肃省胰腺癌发病数据,通过计算年度变化百分比(annual percentage change,APC)和平均年度变化百分比(average annual percentage change,AAPC)分析其变化趋势;构建自回归移动平均(autoregressive integrated moving average,ARIMA)模型和神经网络自回归(neural network autoregression,NNAR)模型,比较两种模型预测精度并预测2022-2026年胰腺癌发病趋势。结果:选择精度更佳的ARIMA模型预测得到2022-2026年甘肃省胰腺癌发病率分别为5.58/10万、6.01/10万、6.44/10万、6.88/10万、7.31/10万。甘肃省胰腺癌发病率在2022-2026年仍保持上升趋势。结论:2022-2026年甘肃省胰腺癌发病率呈现上升趋势,ARIMA模型预测发病趋势具有良好的精度,可以为进一步疾病防控工作提供重要参考依据。

基于ARIMA、GM(1,1)模型的高校ESI学科发展预测研究

[目的/意义]学科建设是高校提升教育质量的关键环节,对科学研究起着重要的支撑作用。采用数学统计建模探索一种科学有效的方法,实现潜力学科入围ESI前1%的时间预测,对于机构学科发展规划有着重要指导意义。[方法/过程]基于ESI数据库,获取目标机构4个潜力学科的被引频次和ESI入围阈值,建立时间序列并创建预测模型:先引入转换系数来去除不同数据库的差异,使其可比,然后分别拟合GM(1,1)模型、ARIMA模型,预测目标学术机构学科被引频次和ESI入围阈值,找到目标机构学科被引频次赶上ESI入围阈值的时间,即预测的入围时间。通过采用平均绝对百分比误差(MAPE)、平均绝对误差(MAE)和均方根误差(RMSE)对模型的拟合预测效果进行评估和比较,根据MAPE、MAE和RMSE三个指标来评价模型拟合及预测效果,以此为学校的学科建设及长远发展规划提供参考依据。[局限]本研究仅局限于目标机构4个学科的数据,尚需获取其他机构、更多学科的数据进行模型预测效果验证。[结果/结论]ARIMA模型的拟合效果和预测效果优于GM(1,1)模型。目标机构的生物学与生物化学学科可能于近期入围ESI前1%;免疫学科有入围ESI前1%学科的潜力,但入围时间可能会稍微滞后;分子生物学与遗传学和神经科学与行为学学科,离入围还有较大差距。

改进GM(1,1)-ARIMA-LR模型天然气产量预测研究

为提高天然气产量在少样本情形下预测的准确性,基于对过去的预测误差进行学习的思想,加入自适应学习因子和组合学习因子以改进模型,构建包含GM(1,1)、ARIMA和LR的集成预测模型。该模型以平均误差百分比为评价指标,依据预测步长变化和过去预测误差对单个模型分别进行动态调整,再建立目标规划模型对各模型进行动态加权。实证结果表明,改进GM(1,1)-ARIMA-LR模型能够更好地提取时间序列的长短时依赖关系,与其它的主流模型相比,其预测精度更高。对近5年的天然气产量进行一步、五步与八步预测,GM(1,1)-ARIMA-LR集成模型预测误差分别为1.187%、3.129%、9.855%。本文运用该模型对2023-2030年中国天然气产量进行预测。

基于ARIMA-LSTM混合模型对传染病的预测分析

传染病一直是科学研究的热点,利用科学的方法控制传染病的传播对整个国家乃至全世界具有举足轻重的作用。文章选取乙类传染病中新型冠状病毒感染数据作为研究对象,搜集了北京市2022年1月至2022年4月新冠感染累计确诊病例数,构成时间序列,基于自回归移动平均模型(ARIMA)和长短期记忆神经网络(LSTM)的混合模型进行预测分析。结果表明,混合模型的预测结果与实际情况基本一致。

基于ARIMA模型的中国青光眼疾病负担预测研究

目的描述和分析1990—2019年中国青光眼疾病负担状况及其变化趋势,并预测2020—2050年中国青光眼疾病负担。方法从2019年全球疾病负担数据库中提取1990—2019年我国青光眼患病率、YLD率和DALYs率等疾病负担指标的数据,基于该数据建立自回归滑动平均模型(ARIMA模型)并预测2020—2050年我国青光眼疾病负担。结果1990—2019年,我国全年龄段青光眼的患病率从49.26/105增长至94.08/105,平均每年增长3.03%,且男性和女性青光眼患病率年均增长分别为2.63%和3.47%;我国全年龄段青光眼伤残所致生命年损失(YLD)率及伤残调整寿命年(DALY)率均由5.68/105提高至7.91/105,年均变化率为1.31%,男性和女性的青光眼YLD率及DALYs率均呈升高趋势,平均每年增长1.01%和1.69%。由ARIMA模型预测的青光眼疾病负担结果显示动态趋势与实际情况基本相同,且用于评估ARIMA模型的预测值与实际值的预测准确性指标:相对误差、平均绝对百分比误差(MAPE)、平均绝对误差(MAE)和均方根误差(RMSE)均很小,预测结果精确。经ARIMA模型预测得到2020年我国青光眼患病率为128.70/105,YLD率为10.63/105,DALYs率为10.63/105,到2050年,预计我国青光眼患病率为942.17/105,YLD率为10.87/105,DALYs率为10.87/105。结论ARIMA模型在拟合我国青光眼疾病负担应用中拟合效果和预测精度良好,可为青光眼疾病负担预测提供借鉴和参考,预计2020—2050年我国青光眼疾病负担呈现上升趋势。

基于ARIMA和LSTM混合模型的林业产品销售价格预测系统研究

为提高林业产品销售价格预测的准确性,提出了基于ARIMA和LSTM混合模型的林业产品销售价格预测系统。系统设计分析和数据库设计确保了系统整体性能。通过对历史数据的预处理,提高了数据质量。利用ARIMA模型和LSTM残差预测进行价格预测,并将两者融合以得到最终预测结果。实验结果显示,该系统的预测误差控制在3以下,具有更高的准确性和较强的可靠性,可为产品价格波动的预测提供参考。