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.

Comparison among the UECM Model, and the Composite Model in Forecasting Malaysian Imports

For more than a century, forecasting models have been crucial in a variety of fields. Models can offer the most accurate forecasting outcomes if error terms are normally distributed. Finding a good statistical model for time series predicting imports in Malaysia is the main target of this study. The decision made during this study mostly addresses the unrestricted error correction model (UECM), and composite model (Combined regression—ARIMA). The imports of Malaysia from the first quarter of 1991 to the third quarter of 2022 are employed in this study’s quarterly time series data. The forecasting outcomes of the current study demonstrated that the composite model offered more probabilistic data, which improved forecasting the volume of Malaysia’s imports. The composite model, and the UECM model in this study are linear models based on responses to Malaysia’s imports. Future studies might compare the performance of linear and nonlinear models in forecasting.

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.

Modeling and Forecasting of Carbon Dioxide Emissions in Bangladesh Using Autoregressive Integrated Moving Average (ARIMA) Models

In the present paper, different Autoregressive Integrated Moving Average (ARIMA) models were developed to model the carbon dioxide emission by using time series data of forty-four years from 1972-2015. The performance of these developed models was assessed with the help of different selection measure criteria and the model having minimum value of these criteria considered as the best forecasting model. Based on findings, it has been observed that out of different ARIMA models, ARIMA (0, 2, 1) is the best fitted model in predicting the emission of carbon dioxide in Bangladesh. Using this best fitted model, the forecasted value of carbon dioxide emission in Bangladesh, for the year 2016, 2017 and 2018 as obtained from ARIMA (0, 2, 1) was obtained as 83.94657 Metric Tons, 89.90464 Metric Tons and 96.28557 Metric Tons respectively.

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.

Comparison of ARIMA and ANN Models Used in Electricity Price Forecasting for Power Market

In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps 8 weeks ahead in the UK electricity market. The half hourly data of historical prices are obtained from UK Reference Price Data from March 22nd to July 14th 2010 and the predictions are derived from a sliding training window with a length of 8 weeks. The ARIMA with various AR and MA orders and the ANN with different numbers of delays and neurons have been established and compared in terms of the root mean square errors (RMSEs) of price forecasts. The experimental results illustrate that the ARIMA (4,1,2) model gives greater improvement over persistence than the ANN (20 neurons, 4 delays) model.

Application of Seasonal Auto-regressive Integrated Moving Average Model in Forecasting the Incidence of Hand-foot-mouth Disease in Wuhan,China

Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average（ARIMA） model for time series analysis was designed in this study. Eighty-four-month（from January 2009 to December 2015） retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination（R^2）, normalized Bayesian Information Criterion（BIC） and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as（1,0,1）（0,1,1）12, with the largest coefficient of determination（R^2=0.743） and lowest normalized BIC（BIC=3.645） value. The residuals of the model also showed non-significant autocorrelations（P_（Box-Ljung（Q））=0.299）. The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.

Predicting the number of visceral leishmaniasis cases in Kashgar, Xinjiang, China using the ARIMA-EGARCH model

Objective:To forecast the visceral leishmaniasis cases using autoregress integrated moving average(ARIMA)and hybrid ARIMAEGARCH model,which offers a scientific basis to control visceral leishmaniasis spread in Kashgar Prefecture of Xinjiang,China.Methods:The data used in this paper are monthly visceral leishmaniasis cases in the Kashgar Prefecture of Xinjiang from 2004 to 2016.The sample data between 2004 and 2015 were used for the estimation to choose the best model and the sample data in 2016 were used for the forecast.Time series of visceral leishmaniasis started on 1 January 2004 and ended on 31 December 2016,consisting of 1790 observations reported in Kashgar Prefecture.Results:For Xinjiang,the total number of reported cases were 2187,the male-to-female ratio of cases was 1:1.42.Patients aged between 0 and 10 years accounted for 82.72%of all reported cases and the largest percentage of visceral leishmaniasis cases was detected among scattered children who accounted for 68.82%.The monthly incidences fitted by ARIMA(2,1,2)(1,1,1)12 model were consistent with the real data collected from 2004 to 2015.However,the predicted cases failed to comply with the observed case number;we then attempted to establish a hybrid ARIMA-EGARCH model to fit visceral leishmaniasis.Finally,the ARIMA(2,1,2)(1,1,1)12-EGARCH(1,1)model showed a good estimation when dealing with volatility clustering in the data series.Conclusions:The combined model has been determined as the best prediction model with the root-mean-square error(RMSE)of 7.23%in the validation phase,which means that this model has high validity and rationality and can be used for short-term prediction of visceral leishmaniasis and could be applied to the prevention and control of the disease.

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.

Forecasting of Cultivated Area in Egyptian Lands Using a Time Series Model for Sustainable Development

The cultivated area is an important component of land resources that has a direct impact on food security. Egyptian cultivated area was estimated to be 3.86 million hectares in 2020. Recently, there has been a decline in cultivated areas, which could be attributed to a number of factors, including climatic changes and urban sprawl, endangering Egyptian sustainable development. So, the aim of the current study was to forecast the values of cultivated areas in Egypt for the next five years using the ARIMA model based on data from 1990 to 2020. The model predicted a decrease in cultivated area in coming years of about 3.06, 3.19, 3.084, 3.082 and 3.21 million hectares, respectively, according to the results. This forecasting will aid the country’s policy development for future land using planning and agricultural production.

基于ARIMA模型的海南省流感样病例发病趋势预测研究

目的应用自回归积分移动平均模型(ARIMA)对海南省流感样病例就诊百分比(ILI%)情况进行预测,以期为流感预防控制措施的制定和卫生资源的合理分配提供科学依据。方法收集2015-2019年海南省6家国家级哨点监测医院的流感样病例每周的数据,使用R软件基于2015年第1周-2019年第26周的流感样病例就诊百分比(ILI%)建立ARIMA模型,并用2019年第27周-第52周的数据作为验证集,对模型的预测能力进行评估和验证。结果海南省6家国家监测哨点医院累计报告门急诊就诊病例总数为6244159例,其中流感样病例数为188143例,流感样病例就诊百分比(ILI%)为3.01%。最优预测模型为ARIMA(0,1,1)(0,1,1)52模型,该模型的赤池信息量准则(AIC)和贝叶斯信息准则(BIC)分别为264.10、273.69,提示该模型拟合较好。运用该模型预测2019年第27周-第52周的ILI%,实际值均落在预测值的95%置信区间范围内。结论ARIMA(0,1,1)(0,1,1)52模型对海南省流感样病例就诊百分比的拟合效果较好,可用于海南省流感的短期预测和动态分析,具有较好预测、预警价值。

基于ARIMA与LSTM的铁路车站客流预测方法比较

精准的客流预测是车站客运组织优化的基础,是提高运营安全和运输效率的有效途径。以江门东站全年进站客流数据为研究对象,分别构建ARIMA时间序列模型与LSTM神经网络模型,从预测精度、计算速度、误差指标评价、模型适应性等方面分析比较两种预测模型对客流预测结果的差异性。结果表明,LSTM模型预测精度和拟合精确度更优,ARIMA模型计算速度更快。研究结果对客流预测方法选择有借鉴意义。

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。

基于ARIMA模型对山东省生猪出场价格变动的预测

猪肉价格保持在合理区间,对于生猪生产和消费至关重要。为预测未来生猪出场价格,推动生猪价格回归合理区间,本文基于2012年11月—2024年2月山东省生猪出场价格,运用ARIMA模型对山东省2024年3月-12月的生猪出场价格变化进行预测。结果表明,该模型拟合效果较好,能准确地预测山东省生猪出场价格短期波动。本文建议政府应该从调控能繁母猪保障底线、完善价格监测预警制度、强化价格风险管理工具、优化生猪库存管理、控制生猪生产成本、落实生猪补贴政策和拓宽生猪贸易等方面采取措施,优化生猪产能调控机制,促进生猪产业的发展。

基于ARIMA构建SWECPX模型解决电商需求预测问题

本文针对电商需求预测问题,基于促销节日因素S(Sale)和仓库因素C(Category),借助Matlab、Excel软件进行数据预处理,以ARIMA时间序列模型为核心,建立SWECPX(Sale Ware Effect Category Product X)模型,使用Matlab软件中的X-12-ARIMA选项等方法进行求解,实现了对商品需求量的准确预测,取得较好的1-wrmape指标测试效果。本文最大的创新点是提出了SWECPX模型,对影响商品需求量的要素S和C进行区分和求解,使对商品需求量预测更加精确,1-wrmape值较高。当每日的商品需求量处于较低水平时,预测效果的提升尤为显著,其预测值几乎与实际值相同。因此,我们期望SWECPX模型可以为电商仓储平台的决策提供切实的参考和借鉴。

Selection of Heteroscedastic Models: A Time Series Forecasting Approach

To overcome the weaknesses of in-sample model selection, this study adopted out-of-sample model selection approach for selecting models with improved forecasting accuracies and performances. Daily closing share prices were obtained from Diamond Bank and Fidelity Bank as listed in the Nigerian Stock Exchange spanning from January 3, 2006 to December 30, 2016. Thus, a total of 2713 observations were explored and were divided into two portions. The first which ranged from January 3, 2006 to November 24, 2016, comprising 2690 observations, was used for model formulation. The second portion which ranged from November 25, 2016 to December 30, 2016, consisting of 23 observations, was used for out-of-sample forecasting performance evaluation. Combined linear (ARIMA) and Nonlinear (GARCH-type) models were applied on the returns series with respect to normal and student-t distributions. The findings revealed that ARIMA (2,1,1)-EGARCH (1,1)-norm and ARIMA (1,1,0)-EGARCH (1,1)-norm models selected based on minimum predictive errors throughout-of-sample approach outperformed ARIMA (2,1,1)-GARCH (2,0)-std and ARIMA (1,1,0)-EGARCH (1,1)-std model chosen through in-sample approach. Therefore, it could be deduced that out-of-sample model selection approach was suitable for selecting models with improved forecasting accuracies and performances.

Modeling and Forecasting of SARS CoV-2 Cases in Sierra Leone

Severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) has been a global threat spreading in Sierra Leone, and many studies are being conducted using various Statistical models to predict the probable evolution of this pandemic. In this paper, we use the autoregressive integrated moving average (ARIMA) model with the aim of forecasting the cumulative confirmed cases of SARS CoV-2 in Sierra Leone. The Akaike Information Criterion (AIC) was applied to the training data as a criterion method to select the best model. In addition, the statistical measure RMSE and MAPE were utilized for testing this data, and the model with the minimum RMSE and MAPE was selected for future forecasting. ARIMA (3, 2, 1) was confirmed to be the optimal model based on the lowest AIC value. This model was then applied to study the trend of SARS CoV-2 from 1st February 2022 to 30th February 2022. The result shows that incidence of SARS CoV-2 from 1st February 2022 to 30th February 2022, increasing growth steep in Sierra Leone (7718.629, 95% confidence limit of 6785.985 - 8651.274).

基于ARIMA和模拟退火算法的电商包裹调运问题研究

随着网络购物方式的日益普及和互联网经济的发展,因促销活动和物流场地停用所导致的运输和分拣包裹成本上升的问题也随之而来。为了降低运营成本、提高运营效率,预测物流场地和运输线路的货物量,为货物调运选择最优路线和方案尤为重要。基于此,建立了ARIMA时间序列预测模型,对物流场地和线路未来的货运量进行预测研究,建立线性规划模型,优化调整突发情况下的货运线路,利用模拟退火算法进行求解,选择影响程度较小的最优线路,降低运输成本。

基于数据预处理的ARIMA模型超短期风电功率预测

为了提高超短期风电功率预测的精确度和稳定性,文章提出了基于预处理数据的ARIMA时间序列自回归差分移动平均模型风电超短期预测算法。以黑龙江省某风电场的实测数据为例,对测风塔数据进行预处理,对风电场数据异常值进行处理,并对风电功率影响因素相关性进行分析,对所得到的数据进行差分处理,从而适应ARIMA模型的预测。结果表明,此方法可以有效提高预测精度和覆盖率。

基于ARIMA模型的股票预测--以中国银行为例

ARIMA模型是一种广泛用于时间序列分析和预测的统计模型。它的核心思想是将时间序列分解成自回归(AR)部分、差分(I)部分和移动平均(MA)部分,从而能够捕捉时间序列中的趋势和周期性。通过中国银行在2023年1月3日至2023年11月30日期间的股票收盘价数据,采用ARIMA模型进行了时间序列分析,对未来31个交易日股票收盘价格进行预测,为投资者提供了有关中国银行股票未来走势的重要信息。