Time Series Analysis and Prediction of COVID-19 Pandemic Using Dynamic Harmonic Regression Models

Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.

Short and Long-Term Time Series Forecasting Stochastic Analysis for Slow Dynamic Processes

This paper intends to develop suitable methods to provide likely scenarios in order to support decision making for slow dynamic processes such as the underlying of agribusiness. A new method to analyze the short- and long-term time series forecast and to model the behavior of the underlying process using nonlinear artificial neural networks (ANN) is presented. The algorithm can effectively forecast the time-series data by stochastic analysis (Monte Carlo) of its future behavior using fractional Gaussian noise (fGn). The algorithm was used to forecast country risk time series for several countries, both for short term that is 30 days ahead and long term 350 days ahead scenarios.

Application of time series modeling to a national reference frame realization

This paper presents an option for modern dynamic terrestrial reference system realization in Uzbekistan for user needs. An additive model is explored to predict patterns of time series and investigate means of constructing forecast time series models in the future. The main components(trend, periodical, and irregular) of the KIUB(DORIS) and KIT3, TASH, MADK, and MTAL(GNSS) international stations coordinate time series were investigated. It was shown that seasonal nonlinear trends occurred both in the height(U) component of all stations and the east(E) component of high mountainous stations such as MTAL and MADK. The seasonal periodical portion of the time series determined from the additive model has a complicated pattern for all sites and can be explained as both hydrological signals in the region and improvement of observational quality. Amplitudes of the best-fitting sinusoids in the North component ranged between 1.73 and 8.76 mm; the East component ranged between 0.82 and 11.92 mm; and the Up component ranged between 3.11 and 40.81 mm. Regression analysis of the irregular portion of the height component of the two techniques at the Kitab station using tropospheric parameters(pressure and temperature) was confirmed as only 57% of the stochastic portion of the time series.

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.

Simulation of the growth ring density of Larix olgensis plantation wood with the ARMA model

Because growth ring data have temporal features, time series analysis can be used to simulate and reveal changes in the life of a tree and contribute to plantation management. In this study, the autoregressive(AR) and moving average modeling method was used to simulate the time series for growth ring density in a larch plantation with different initial planting densities. We adopted the Box–Jenkins method for the modeling, which was initially based on an intuitive analysis of sequence graphs followed by the augmented Dickey–Fuller stationarity test. The order p and q of the ARMA(p, q) model was determined based on the autocorrelation and partial correlation coefficient figure truncated on the respective order.Through the residual judgment, the model AR(2) was only fitted to the larch growth ring density series for the plantation with the 1.5 9 2.0 m^2 initial planting density.Because the residuals series for the other three series was not shown as a white noise sequence, the modeling was rerun. Larch wood from the initial planting density of2.0 9 2.0 m^2 was modeled by ARMA(2, 1), and ARMA((1, 5), 3) fitted to the 2.5 9 2.5 m^2 initial planting density,and the 3.0 9 3.0 m^2 was modeled by AR(1, 2, 5).Although the ARMA modeling can simulate the change in growth ring density, data for the different growth ring time series were described by different models. Thus, time series modeling can be suitable for growth ring data analysis, revealing the time domain and frequency domain of growth ring data.

基于SPSS Modeler的气象数据分析

随着信息化的普及,气象信息化的程度日益提高。气象部门积累了大量的气象数据,如何充分利用这些数据,获取其中蕴藏的价值,已经成为大数据时代面临的主要任务。基于SPSS Modeler对某站点的气象数据进行分析,介绍了数据加载、数据抽取、离群值极值处理、数据分析、数据挖掘等步骤。

基于改进时序胶囊网络的油藏生产动态分析模型

我国许多油田的主力开发区块已逐渐进入高含水期,地下油藏复杂,含水量逐步上升,产油量下降。提高对现阶段油田开发生产规律和开采状况的准确认识,对研究油田生产动态变化规律以及制定油田开发策略具有重要意义。针对油田生产动态变化规律的问题,本文提出一种基于改进时序胶囊预测的油藏动态分析模型。首先,应用双向门控循环单元来捕捉油田数据中的时序特征,提升模型对时序信息的建模能力;其次,用多头注意力深度卷积层捕捉初级时序特征信息,高效地提取序列的长距离依赖关系和复杂特征表示;最后,在动态路由算法中引入注意力机制,让高级胶囊更好地关注重要特征,从而提高信息传递的效率和准确性。为验证本文模型有效性,将油田的时序数据作为输入,通过改进胶囊网络模型输出预测日产油量。将改进的胶囊网络与ResNet、LeNet5等9种模型进行对比。实验结果表明,改进后的胶囊网络的预测精度更高,可达到94.5%。

融合情绪分析和Informer-ARIMA模型的比特币价格预测方法

相较于传统金融产品,比特币价格更容易受到情绪的影响而展现出更高的波动性,为此价格预测具有极高的研究价值。为提高比特币价格预测的精准度,文章在预测模型中引入情绪指标,构建融合情绪分析和Informer-ARIMA模型的预测方法。从多维度分析价格时间序列的随机波动、循环变化、周期变化等变化规律,对比特币的价格进行有效预测。测试结果表明,融合情绪分析的Informer-ARIMA模型性能更优,验证了所提方法的可行性和有效性。

深度老龄化省份人口老龄化城乡倒置时空演变及预测趋势

人口老龄化城乡倒置现象已成为城乡协调与我国健康可持续发展面临的重要挑战。本文基于2000—2022年我国19个深度老龄化省份,剖析其人口老龄化城乡倒置的时空演变,同时运用BP时间序列预测模型对2023—2035年人口老龄化城乡倒置度进行预测。研究结果表明:(1)时序演变方面,到2020年19个深度老龄化省份均出现城乡倒置的现象,城乡倒置度年增长率在时序上具有阶段性,在空间上具有区域异质性,研究期末呈南北差异性;(2)空间分布方面,在2000—2022年期间深度老龄化省份人口老龄化城乡倒置呈现“东北—西南”方向分布,分布重心具有“先西南,后东北”的移动趋势;(3)预测趋势方面,预测2023—2035年人口老龄化城乡倒置的重心有东部地区向西部地区转移的趋势。最终,根据研究结果,提出对应建议,以期更好地应对我国未来人口老龄化城乡倒置的浪潮,推动城乡协调发展。

时间序列模型在经济分析中的应用——陕西省GDP分析与预测

相对精准的GDP分析与预测可以为国家或地区在进行经济发展战略部署及相关发展措施的制定方面提供重要参考依据。文章借助时间序列的相关定义和方法,分析和模拟了影响陕西省GDP的主要因素及其与第一、二、三产业的关系,并进一步采用数据分析方法,建立不同的模型并加以比较,得出了最佳的预测模型,并对第一、二、三产业与GDP进行了预测。

基于时间序列分析的某地区中长期负荷预测研究

随着我国经济蓬勃增长,城市不断扩张,用电需求迅速攀升,电力已成为各领域不可或缺的关键要素。精确的负荷预测可以最大化资源的有效利用,确保电力供应的可靠性,有助于促进电力体系的可持续发展。介绍了对时间序列分析方法,根据某地区电量负荷增长情况,提出了以时间序列分析为基础的负荷预测模型。基于某地区2003—2022年供电量作为历史数据,在时间序列法中采用ARIMA模型与指数平滑法这两种方法,对2023—2028年负荷量进行预测,为某地区未来电网规划提供数据基础。

A Fuzzy Time Series Model Based on Improved Fuzzy Function and Cluster Analysis Problem

Based on the improvement in establishing the relations of data,this study proposes a new fuzzy time series model.In this model,the suitable number of fuzzy sets and their specific elements are determined automatically.In addition,using the percentage variations of series between consecutive periods of time,we build the fuzzy function.Incorporating all these improvements,we have a new fuzzy time series model that is better than many existing ones through the well-known data sets.The calculation of the proposed model can be performed conveniently and efficiently by a MATLAB procedure.The proposed model is also used in forecasting for an urgent problem in Vietnam.This application also shows the advantages of the proposed model and illustrates its effectiveness in practical application.

Monthly and seasonal streamflow forecasting of large dryland catchments in Brazil

Streamflow forecasting in drylands is challenging.Data are scarce,catchments are highly humanmodified and streamflow exhibits strong nonlinear responses to rainfall.The goal of this study was to evaluate the monthly and seasonal streamflow forecasting in two large catchments in the Jaguaribe River Basin in the Brazilian semi-arid area.We adopted four different lead times:one month ahead for monthly scale and two,three and four months ahead for seasonal scale.The gaps of the historic streamflow series were filled up by using rainfall-runoff modelling.Then,time series model techniques were applied,i.e.,the locally constant,the locally averaged,the k-nearest-neighbours algorithm(k-NN)and the autoregressive(AR)model.The criterion of reliability of the validation results is that the forecast is more skillful than streamflow climatology.Our approach outperformed the streamflow climatology for all monthly streamflows.On average,the former was 25%better than the latter.The seasonal streamflow forecasting(SSF)was also reliable(on average,20%better than the climatology),failing slightly only for the high flow season of one catchment(6%worse than the climatology).Considering an uncertainty envelope(probabilistic forecasting),which was considerably narrower than the data standard deviation,the streamflow forecasting performance increased by about 50%at both scales.The forecast errors were mainly driven by the streamflow intra-seasonality at monthly scale,while they were by the forecast lead time at seasonal scale.The best-fit and worst-fit time series model were the k-NN approach and the AR model,respectively.The rainfall-runoff modelling outputs played an important role in improving streamflow forecasting for one streamgauge that showed 35%of data gaps.The developed data-driven approach is mathematical and computationally very simple,demands few resources to accomplish its operational implementation and is applicable to other dryland watersheds.Our findings may be part of drought forecasting systems and potentially help allocating water months in advance.Moreover,the developed strategy can serve as a baseline for more complex streamflow forecast systems.

The Future Human Lifespan: A Study on Italian Population

In the latter part of the 20th century, continued improvements in living standards, health behaviors, and medical care reduced mortality and produced amazing advances in life expectancy. These trends, followed by all industrial nations, decidedly affect the financial position of an insurance company, interested in the construction of updated life tables. The approach to this problem is faced in this paper by using the Lee-Carter methodology. In particular, in the present work, we are interested in modeling and forecasting mortality and life expectancy on a period basis through the use of a stochastic forecasting method which uses time-series models to make long-term forecasts.

A Hybrid Methodology for Short Term Temperature Forecasting

Developing a reliable weather forecasting model is a complicated task, as it requires heavy IT resources as well as heavy investments beyond the financial capabilities of most countries. In Lebanon, the prediction model used by the civil aviation weather service at Rafic Hariri International Airport in Beirut (BRHIA) is the ARPEGE model, (0.5) developed by the weather service in France. Unfortunately, forecasts provided by ARPEGE have been erroneous and biased by several factors such as the chaotic character of the physical modeling equations of some atmospheric phenomena (advection, convection, etc.) and the nature of the Lebanese topography. In this paper, we proposed the time series method ARIMA (Auto Regressive Integrated Moving Average) to forecast the minimum daily temperature and compared its result with ARPEGE. As a result, ARIMA method shows better mean accuracy (91%) over the numerical model ARPEGE (68%), for the prediction of five days in January 2017. Moreover, back to five months ago, in order to validate the accuracy of the proposed model, a simulation has been applied on the first five days of August 2016. Results have shown that the time series ARIMA method has offered better mean accuracy (98%) over the numerical model ARPEGE (89%) for the prediction of five days of August 2016. This paper discusses a multiprocessing approach applied to ARIMA in order to enhance the efficiency of ARIMA in terms of complexity and resources.

Short Term Wind Power Forecasting Using Autoregressive Integrated Moving Average Approach

Wind energy is one of the most promising electricity generating sources as a clean and free alternate compared with the conventional power plants and due to the volatility feature in the wind speeds it will reflect some problems in power systems reliability particularly if the system is deeply penetrated by wind farms. Therefore, wind power forecasting issue become and is still an important scope that will help in ED （economic dispatch）, UC （unit commitment） purposes to get more reliable and economic systems. This paper introduces short term wind power forecasting model, based on ARIMA （autoregressive integrated moving average） which will be applied to hourly wind data from Zaafarana 5 project in Egypt. The proposed model successfully outperforms the persistence model with significant improvement up to 6 h ahead.

基于贝叶斯方法与可解释机器学习的负荷特性分析与预测

使用机器学习模型和方法进行短期负荷预测,虽能提升负荷预测的整体精度,但在极端天气、节假日等小样本预测场景中,对比基于专家经验的人工预测无明显优势。为充分结合预测业务人员的经验知识与机器学习的推理泛化能力,提出了一种基于贝叶斯时变系数(BTVC)与CatBoost模型的可解释负荷预测框架。首先,结合数据与专家知识,构建BTVC模型进行预测,获得各影响因子、趋势及周期因素的负荷分量。其次,将上述结果与常规特征进行组合,作为CatBoost回归模型的输入,进行最终预测。然后,使用事后模型解释框架(SHAP)进行归因分析,框架输出的定量关系可供负荷预测业务人员参考,使其开发出更有效的特征,进一步提高预测效果。最后,以某地区实际电网负荷数据为例,验证所提负荷预测与结果分析框架的有效性。

SSA-SARIMA组合模型的桥梁健康状态预测

为了预测桥梁未来状态,考虑奇异谱分析(SSA)和季节性差分自回归滑动平均(SARIMA)模型的特点,提出将二者结合以实现优势互补。利用SSA提取数据中的趋势项、季节项及高频项;采用SSA和SARIMA模型对每项分别进行预测,以选出最优方案重组并验证该方法的有效性;最后,将该方法与SSA和SARIMA模型进行比较,结果表明SSA-SARIMA的组合模型对预测结果的精度有明显的提高。用该方法对实桥数据进行了处理分析,取得了较好的效果。

多变国际形势下新能源汽车销量分析——基于突发因素的复合预测模型

2022年初,国际政治经济局势发生突变,能源产业分布呈现新的格局,以石油为代表的化石能源价格激增,从而引起了后继新能源汽车销量的异常及产能的格局变化。本文基于时间序列分析,在分析了新能源汽车月销量序列的性质后,引入Logistic模型作为修正,建立了一个复合预测模型,尝试分析在外部突发因素影响下新能源汽车销量的变化,并预测其未来走势。本文在考虑到市场本身发展及外部突发因素影响的同时,又兼顾市场发展的延续性,更具合理性和适用性,从理论和实际两方面更好解释了相关长尾突发事件对整体数据变化的影响,在多种预测情形下具有重要意义。

时间序列分析在麻疹疫情预测预警中的应用研究

目的研究时间序列分析在传染病疫情预测预警中的应用,并探讨提高模型预测准确性和实用性的思路。方法以1952年1月至2006年12月江苏省麻疹发病资料建立时间序列分析模型,以2007年的发病资料作为模型预测效果的考核样本,然后将2007年的实际数据加入到原始序列中建立模型用2008年的数据来考核,并对以年为单位的发病资料进行分析和讨论。先采用差分方法对序列资料进行平稳化,然后进行定阶并估计参数,建立ARIMA模型,最后对预测结果进行分析和评价,探讨对疫情进行预警的方法和思路。结果江苏省麻疹的发病趋势自2006年明显上升之后保持平稳,但有小幅波动,这与实际情况吻合。检验表明模型结果具有较好的参考价值。结论用时间序列分析对传染病发病情况的拟合结果满意,预测和预警效果良好,为传染病防治提供了依据。