The research conducted prediction on changes of atmosphere pollution during July 9, 2014-July 22, 2014 with SPSS based on monitored data of O3 in 13 successive weeks from 6 sites in Baoding City and demonstrated predi...The research conducted prediction on changes of atmosphere pollution during July 9, 2014-July 22, 2014 with SPSS based on monitored data of O3 in 13 successive weeks from 6 sites in Baoding City and demonstrated prediction effect of ARIMA model is good by Ljung-Box Q-test and R2, and the model can be used for prediction on future atmosphere pollutant changes.展开更多
[Objective] The aim was to establish drought forecasting model with high precision. [Method] With an ARIMA regression model, the research performed Palmer Drought mode（PDSI） time series modeling analysis of Henan Pr...[Objective] The aim was to establish drought forecasting model with high precision. [Method] With an ARIMA regression model, the research performed Palmer Drought mode（PDSI） time series modeling analysis of Henan Province based on PDSI time series and DPS（Data Processing Software） in order to build drought forecasting model. [Result] It is feasible to perform drought forecasting with appropriate parameters. [Conclusion] ARIMA model is practical and more precise in PDSI-based drought analysis and forecasting.展开更多
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...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.展开更多
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 s...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.展开更多
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 m...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.展开更多
In this study, the number of sheep and goats in Turkey were analysed by time series analysis method, and the number of great cattle for next years predicted through the most appropriate time series model.Time series w...In this study, the number of sheep and goats in Turkey were analysed by time series analysis method, and the number of great cattle for next years predicted through the most appropriate time series model.Time series was formed using the data on the number of sheep and goats belonging to the period between 1930 and 2014 in Turkey It was determined through autocorrelation function graphic that the series weren＇t stationary at first, but they became stationary after their first difference were calculated. A stagnancy test was performed through extended Dickey-Fuller test. So as to determine the suitability of the model, it was reviewed if autocorrelation and partial autocorrelation graphs were white noise series and also the results of Box-Ljung test were reviwed. Through the ＂tested models, the model estimations, of which parameter estimates were significant and Akaike information criterion （AIC） was the smallest, were performed. The most appropriate model in terms of both the number of sheep and goats is first-level integrated moving average model stated as ARIMA（0,1,1）. In this model, it was estimated that there would be an increase in the number of sheep and goats in Turkey between the years of 2015 and 2020, however, the increase in the number of sheep would be more than the increase in the number of goats.展开更多
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 201...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.展开更多
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 con...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.展开更多
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 ...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.展开更多
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 param...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.展开更多
校园用水数据,既有趋势性又有季节性。为了准确地对智能水表收集的用水数据进行异常点分析,从而检测预估管网漏损问题,研究对用水数据进行了相关检验,并选择了合适的自回归差分移动平均模型(Autoregressive Integrated Moving Average M...校园用水数据,既有趋势性又有季节性。为了准确地对智能水表收集的用水数据进行异常点分析,从而检测预估管网漏损问题,研究对用水数据进行了相关检验,并选择了合适的自回归差分移动平均模型(Autoregressive Integrated Moving Average Model,ARIMA)模型。基于Chen-Liu迭代算法,研究利用R软件进行编程,成功识别了用水数据中的异常点位置、类型、异常效应的大小,以及调整后的时间序列等,由此预估管网漏损可能出现的日期和位置。研究发现,基于ARIMA时间序列模型对用水数据进行异常点的检测较为准确,且输出的异常点类型可以区分异常点是人为因素造成还是由管网漏损问题造成,进而预估管网漏损问题,这为供水行业漏损管理模式提供了一种新的方向。展开更多
基金Supported by Student Research Fund of Agricultural University of Hebei(cxzr2014023)Technology Fund of Agricultural University of Hebei(ZD201406)~~
文摘The research conducted prediction on changes of atmosphere pollution during July 9, 2014-July 22, 2014 with SPSS based on monitored data of O3 in 13 successive weeks from 6 sites in Baoding City and demonstrated prediction effect of ARIMA model is good by Ljung-Box Q-test and R2, and the model can be used for prediction on future atmosphere pollutant changes.
文摘[Objective] The aim was to establish drought forecasting model with high precision. [Method] With an ARIMA regression model, the research performed Palmer Drought mode（PDSI） time series modeling analysis of Henan Province based on PDSI time series and DPS（Data Processing Software） in order to build drought forecasting model. [Result] It is feasible to perform drought forecasting with appropriate parameters. [Conclusion] ARIMA model is practical and more precise in PDSI-based drought analysis and forecasting.
文摘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.
文摘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.
文摘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.
文摘In this study, the number of sheep and goats in Turkey were analysed by time series analysis method, and the number of great cattle for next years predicted through the most appropriate time series model.Time series was formed using the data on the number of sheep and goats belonging to the period between 1930 and 2014 in Turkey It was determined through autocorrelation function graphic that the series weren＇t stationary at first, but they became stationary after their first difference were calculated. A stagnancy test was performed through extended Dickey-Fuller test. So as to determine the suitability of the model, it was reviewed if autocorrelation and partial autocorrelation graphs were white noise series and also the results of Box-Ljung test were reviwed. Through the ＂tested models, the model estimations, of which parameter estimates were significant and Akaike information criterion （AIC） was the smallest, were performed. The most appropriate model in terms of both the number of sheep and goats is first-level integrated moving average model stated as ARIMA（0,1,1）. In this model, it was estimated that there would be an increase in the number of sheep and goats in Turkey between the years of 2015 and 2020, however, the increase in the number of sheep would be more than the increase in the number of goats.
文摘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.
基金Supported by National Natural Science Foundation of China (70803005)Special fund of the Baisc Sicentific Research Fund for Central Colleges and Universisties(RW2010-6)
文摘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.
基金Supported by the Project of Beijing Municipal Commission of City Management(SC1708A)
文摘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.
文摘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.
文摘校园用水数据,既有趋势性又有季节性。为了准确地对智能水表收集的用水数据进行异常点分析,从而检测预估管网漏损问题,研究对用水数据进行了相关检验,并选择了合适的自回归差分移动平均模型(Autoregressive Integrated Moving Average Model,ARIMA)模型。基于Chen-Liu迭代算法,研究利用R软件进行编程,成功识别了用水数据中的异常点位置、类型、异常效应的大小,以及调整后的时间序列等,由此预估管网漏损可能出现的日期和位置。研究发现,基于ARIMA时间序列模型对用水数据进行异常点的检测较为准确,且输出的异常点类型可以区分异常点是人为因素造成还是由管网漏损问题造成,进而预估管网漏损问题,这为供水行业漏损管理模式提供了一种新的方向。
