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
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
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
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
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.
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.
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.