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
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
Precipitation is very important for both the environment and its inhabitants. Agricultural activities mostly depend on precipitation and its availability. Therefore, the ability to predict future precipitation values ...Precipitation is very important for both the environment and its inhabitants. Agricultural activities mostly depend on precipitation and its availability. Therefore, the ability to predict future precipitation values at specific stations is key for environmental and agricultural decision making. This research developed Autoregressive Integrated Moving Average (ARIMA) models for selected stations with Integrated component and Autoregressive Moving Average (ARMA) for selected stations without Integrated component at Louisiana State. The ARIMA module is represented as ARIMA(p, d, q)(P,D,Q). The selected lag order for the Autoregressive (AR) component is represented with p and P for seasonal AR component, while the integrated form (number of times data were differenced) is d and D for seasonal differencing, and the Moving Average (MA) lag order is q and Q for seasonal MA component. Data from 1950 to 2020 were employed in this research. Results of the analysis indicated that Baton Rouge (ARIMA (0,1,1) (0,0,2)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), Abbeville (ARMA (0,0,1) (0,0,2)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), Monroe Regional (ARMA (0,0,1) (0,0,0)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), New Orleans Airport (ARMA (1,0,0) (0,0,2)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), Alexandria (ARMA (1,0,1) (0,0,0)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), Logansport (ARIMA (0,1,2) (0,0,0)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), New Orleans Audubon (ARMA (1,0,0) (0,0,0)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), Lake Charles Airport (ARMA (2,0,2) (0,0,0)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">) are the best ARIMA models for predicting precipitation in Louisiana. The models were used to predict the average monthly rainfall at each station. The highest precipitation observed in Louisiana was recorded in 1991. The Precipitation in Louisiana fluctuated over the years but has adopted a decreasing trend from the year 2000 to 2020. It was recommended that the government, researchers, and individuals take note of these models to make future plans to help increase the production of agricultural commodities and prevent destructions caused by excessive precipitation.展开更多
Time-series-based forecasting is essential to determine how past events affect future events. This paper compares the performance accuracy of different time-series models for oil prices. Three types of univariate mode...Time-series-based forecasting is essential to determine how past events affect future events. This paper compares the performance accuracy of different time-series models for oil prices. Three types of univariate models are discussed: the exponential smoothing (ES), Holt-Winters (HW) and autoregressive intergrade moving average (ARIMA) models. To determine the best model, six different strategies were applied as selection criteria to quantify these models’ prediction accuracies. This comparison should help policy makers and industry marketing strategists select the best forecasting method in oil market. The three models were compared by applying them to the time series of regular oil prices for West Texas Intermediate (WTI) crude. The comparison indicated that the HW model performed better than the ES model for a prediction with a confidence interval of 95%. However, the ARIMA (2, 1, 2) model yielded the best results, leading us to conclude that this sophisticated and robust model outperformed other simple yet flexible models in oil market.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 accuracy of predicting the Producer Price Index(PPI)plays an indispensable role in government economic work.However,it is difficult to forecast the PPI.In our research,we first propose an unprecedented hybrid mode...The accuracy of predicting the Producer Price Index(PPI)plays an indispensable role in government economic work.However,it is difficult to forecast the PPI.In our research,we first propose an unprecedented hybrid model based on fuzzy information granulation that integrates the GA-SVR and ARIMA(Autoregressive Integrated Moving Average Model)models.The fuzzy-information-granulation-based GA-SVR-ARIMA hybrid model is intended to deal with the problem of imprecision in PPI estimation.The proposed model adopts the fuzzy information-granulation algorithm to pre-classification-process monthly training samples of the PPI,and produced three different sequences of fuzzy information granules,whose Support Vector Regression(SVR)machine forecast models were separately established for their Genetic Algorithm(GA)optimization parameters.Finally,the residual errors of the GA-SVR model were rectified through ARIMA modeling,and the PPI estimate was reached.Research shows that the PPI value predicted by this hybrid model is more accurate than that predicted by other models,including ARIMA,GRNN,and GA-SVR,following several comparative experiments.Research also indicates the precision and validation of the PPI prediction of the hybrid model and demonstrates that the model has consistent ability to leverage the forecasting advantage of GA-SVR in non-linear space and of ARIMA in linear space.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
In this paper, the Box-Jenkins modelling procedure is used to determine an ARIMA model and go further to forecasting. The mobile cellular subscription data for the study were taken from the administrative data submitt...In this paper, the Box-Jenkins modelling procedure is used to determine an ARIMA model and go further to forecasting. The mobile cellular subscription data for the study were taken from the administrative data submitted to the Zambia Information and Communications Technology Authority (ZICTA) as quarterly returns by all three mobile network operators Airtel Zambia, MTN Zambia and Zamtel. The time series of annual figures for mobile cellular subscription for all mobile network operators is from 2000 to 2014 and has a total of 15 observations. Results show that the ARIMA (1, 2, 1) is an adequate model which best fits the mobile cellular subscription time series and is therefore suitable for forecasting subscription. The model predicts a gradual rise in mobile cellular subscription in the next 5 years, culminating to about 9.0% cumulative increase in 2019.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
Stock market prediction has long been an area of interest for investors, traders, and researchers alike. Accurate forecasting of stock prices is crucial for financial decision-making and risk management. This paper pr...Stock market prediction has long been an area of interest for investors, traders, and researchers alike. Accurate forecasting of stock prices is crucial for financial decision-making and risk management. This paper presents a novel approach to predict stock prices by integrating Autoregressive Integrated Moving Average (ARIMA) and Exponential smoothing and Machine Learning (ML) techniques. Our study aims to enhance the predictive accuracy of stock price forecasting, which can significantly impact investment strategies and economic growth in this research paper implement the ARIMAML proposed method to predict the stock prices for Investment Bank of Iraq.展开更多
We use the Autoregressive Integrated Moving Average(ARIMA)model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models...We use the Autoregressive Integrated Moving Average(ARIMA)model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models’predictions.We first examine the stationary of the dataset and use ARIMA(0,1,1)to make predictions about the stock price during the pandemic,then we train the Prophet model using the stock price before January 1,2021,and predict the stock price after January 1,2021,to present.We also make a comparison of the prediction graphs of the two models.The empirical results show that the ARIMA model has a better performance in predicting Google’s stock price during the pandemic.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
Infrasound,known for its strong penetration and low attenuation,is extensively used in monitoring and warning systems for debris flows.Here,a debris-flow forecasting method was proposed by combining infrasound-based v...Infrasound,known for its strong penetration and low attenuation,is extensively used in monitoring and warning systems for debris flows.Here,a debris-flow forecasting method was proposed by combining infrasound-based variational mode decomposition and Autoregressive Integrated Moving Average(ARIMA)model.High-precision infrasound sensor was utilized in experiments to record signals under twelve varying conditions of debris flow volume and velocity.Variational mode decomposition was performed on the detected raw signals,and the optimal decomposition scale and penalty factor were obtained through the sparrow search algorithm.The Hilbert transform,rescaled range analysis,power spectrum analysis,and Pearson correlation coefficients judgment criteria were employed to separate and reconstruct the signals.Based on the reconstructed infrasound signals,an ARIMA model was constructed to forecast the trend of debris flow infrasound signal.Results reveal that the Hilbert transform effectively separated noise,and the predictive model’s results fell within a 95%confidence interval.The Mean Absolute Percentage Error(MAPE)across four experiments were 4.87%,5.23%,5.32%and 4.47%,respectively,showing a satisfactory accuracy and providing an alternative for predicting debris flow by infrasound signals.展开更多
Background & Objectives: Sustainable Development Goals (SDGs) are set up as a part of the Post Millennium Development Goals (MDGs). Then it becomes essential to review the achievement of the MDGs in India and less...Background & Objectives: Sustainable Development Goals (SDGs) are set up as a part of the Post Millennium Development Goals (MDGs). Then it becomes essential to review the achievement of the MDGs in India and lessons learned to incorporate into the SDGs. The present study reviews and predicts different components of under-five mortality rate beyond 2015 to assess the present situation and to determine the future possibilities of achieving the new targets for SDGs in India. Data and Methods: It uses available time series data on different components of U5MR from the India’s Sample Registration System (SRS). Autoregressive Integrated Moving Averages (ARIMA) model has been taken as the method of time series analysis to forecast the mortality rates beyond 2015. Results: There is a consistent pattern of faster decline in the under-five mortality compared with the neonatal mortality rate across all major states in India although neonatal mortality contributes largest share in under-five mortality. Again, share of neonatal death among under-five death is increasing steadily over the future projected years. This indicates very slow progress of reduction in neonatal mortality. Stimulating efforts with new intervention programmes will be needed to focus more on lowering neonatal mortality particularly in rural India.展开更多
Change trend of Chinese urban residents＇ per capita food-nitrogen annual consumption from 1981 to 2007 was analyzed and predicted by using ARIMA time-series model in order to reveal the change of urban food-nitrogen ...Change trend of Chinese urban residents＇ per capita food-nitrogen annual consumption from 1981 to 2007 was analyzed and predicted by using ARIMA time-series model in order to reveal the change of urban food-nitrogen consumption during the China＇s urbanization process.Results showed that after 1980s,the annual consumption of Chinese urban residents＇ food-nitrogen had a change trend of ＂ increase-decrease-increase＂ and generally presented as a slight increasing trend;With the acceleration of rapid economic development and urbanization process,Chinese urban residents＇ food-nitrogen consumption will still keep a rising trend in future,and also has a large rising space.展开更多
Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent t...Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model’s parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1st to July7th 2010.展开更多
This study focuses on the Indian gold futures market where primary participants hold sentimental value for the underlying asset and are globally ranked number two in terms of the largest private holdings in the physic...This study focuses on the Indian gold futures market where primary participants hold sentimental value for the underlying asset and are globally ranked number two in terms of the largest private holdings in the physical form.The trade of gold futures relates to seasons,festivity,and government policy.So,the paper will discuss seasonality and intervention in the analysis.Due to non-constant variance,we will also use the standard variance stabilization transformation method and the ARIMA/GARCH modelling method to compare the forecast performance on the gold futures prices.The results from the analysis show that while the standard variance transformation method may provide better point forecast values,the ARIMA/GARCH modelling method provides much shorter forecast intervals.The empirical results of this study which rationalise the effect of seasonality in the Indian bullion derivative market have not been reported in literature.展开更多
Quantifying the potential market of sports licenses is key in order for National Governing Bodies of sport （NGBs） to be able to design good strategic planning. We compared the classical methods of univariate predict...Quantifying the potential market of sports licenses is key in order for National Governing Bodies of sport （NGBs） to be able to design good strategic planning. We compared the classical methods of univariate prediction and the Autoregressive Integrated Moving Average （ARIMA） methods. Reliability of the available data was verified with the Time Series Regression with ARIMA Noise, Missing and Outliers （TRAMO） method, and the existence of a trend was verified using Daniel＇s test. For the purposes of this study--the researches collected and analysed secondary data from a 40-year series in 45 sports in Spain covering a very long period of time in a variety of sport disciplines. The study shows that, with the available data, short- and mid-term forecasting is possible in a number of sports, but not in all of them. It also proves that Holt＇s classical method of exponential smoothing is the one that yields best results. Golf, Basketball, Athletics and Hunting NGB show worrying prospects of decline levels and need an immediate change in the strategic plans. Other than for forecasting the evolution of athletes in the mid-term in order to improve strategic planning in NGBs, the present findings can be useful for public authorities to define their aid policies for NGBs, and they can also help companies in the industry to anticipate market developments.展开更多
Data Mining has become an important technique for the exploration and extraction of data in numerous and various research projects in different fields (technology, information technology, business, the environment, ec...Data Mining has become an important technique for the exploration and extraction of data in numerous and various research projects in different fields (technology, information technology, business, the environment, economics, etc.). In the context of the analysis and visualisation of large amounts of data extracted using Data Mining on a temporary basis (time-series), free software such as R has appeared in the international context as a perfect inexpensive and efficient tool of exploitation and visualisation of time series. This has allowed the development of models, which help to extract the most relevant information from large volumes of data. In this regard, a script has been developed with the goal of implementing ARIMA models, showing these as useful and quick mechanisms for the extraction, analysis and visualisation of large data volumes, in addition to presenting the great advantage of being applied in multiple branches of knowledge from economy, demography, physics, mathematics and fisheries among others. Therefore, ARIMA models appear as a Data Mining technique, offering reliable, robust and high-quality results, to help validate and sustain the research carried out.展开更多
This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characte...This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.展开更多
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.
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Precipitation is very important for both the environment and its inhabitants. Agricultural activities mostly depend on precipitation and its availability. Therefore, the ability to predict future precipitation values at specific stations is key for environmental and agricultural decision making. This research developed Autoregressive Integrated Moving Average (ARIMA) models for selected stations with Integrated component and Autoregressive Moving Average (ARMA) for selected stations without Integrated component at Louisiana State. The ARIMA module is represented as ARIMA(p, d, q)(P,D,Q). The selected lag order for the Autoregressive (AR) component is represented with p and P for seasonal AR component, while the integrated form (number of times data were differenced) is d and D for seasonal differencing, and the Moving Average (MA) lag order is q and Q for seasonal MA component. Data from 1950 to 2020 were employed in this research. Results of the analysis indicated that Baton Rouge (ARIMA (0,1,1) (0,0,2)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), Abbeville (ARMA (0,0,1) (0,0,2)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), Monroe Regional (ARMA (0,0,1) (0,0,0)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), New Orleans Airport (ARMA (1,0,0) (0,0,2)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), Alexandria (ARMA (1,0,1) (0,0,0)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), Logansport (ARIMA (0,1,2) (0,0,0)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), New Orleans Audubon (ARMA (1,0,0) (0,0,0)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), Lake Charles Airport (ARMA (2,0,2) (0,0,0)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">) are the best ARIMA models for predicting precipitation in Louisiana. The models were used to predict the average monthly rainfall at each station. The highest precipitation observed in Louisiana was recorded in 1991. The Precipitation in Louisiana fluctuated over the years but has adopted a decreasing trend from the year 2000 to 2020. It was recommended that the government, researchers, and individuals take note of these models to make future plans to help increase the production of agricultural commodities and prevent destructions caused by excessive precipitation.
文摘Time-series-based forecasting is essential to determine how past events affect future events. This paper compares the performance accuracy of different time-series models for oil prices. Three types of univariate models are discussed: the exponential smoothing (ES), Holt-Winters (HW) and autoregressive intergrade moving average (ARIMA) models. To determine the best model, six different strategies were applied as selection criteria to quantify these models’ prediction accuracies. This comparison should help policy makers and industry marketing strategists select the best forecasting method in oil market. The three models were compared by applying them to the time series of regular oil prices for West Texas Intermediate (WTI) crude. The comparison indicated that the HW model performed better than the ES model for a prediction with a confidence interval of 95%. However, the ARIMA (2, 1, 2) model yielded the best results, leading us to conclude that this sophisticated and robust model outperformed other simple yet flexible models in oil market.
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.
基金This work was supported by Hainan Provincial Natural Science Foundation of China[2018CXTD333,617048]The National Natural Science Foundation of China[61762033,61702539]+1 种基金Hainan University Doctor Start Fund Project[kyqd1328]Hainan University Youth Fund Project[qnjj1444].
文摘The accuracy of predicting the Producer Price Index(PPI)plays an indispensable role in government economic work.However,it is difficult to forecast the PPI.In our research,we first propose an unprecedented hybrid model based on fuzzy information granulation that integrates the GA-SVR and ARIMA(Autoregressive Integrated Moving Average Model)models.The fuzzy-information-granulation-based GA-SVR-ARIMA hybrid model is intended to deal with the problem of imprecision in PPI estimation.The proposed model adopts the fuzzy information-granulation algorithm to pre-classification-process monthly training samples of the PPI,and produced three different sequences of fuzzy information granules,whose Support Vector Regression(SVR)machine forecast models were separately established for their Genetic Algorithm(GA)optimization parameters.Finally,the residual errors of the GA-SVR model were rectified through ARIMA modeling,and the PPI estimate was reached.Research shows that the PPI value predicted by this hybrid model is more accurate than that predicted by other models,including ARIMA,GRNN,and GA-SVR,following several comparative experiments.Research also indicates the precision and validation of the PPI prediction of the hybrid model and demonstrates that the model has consistent ability to leverage the forecasting advantage of GA-SVR in non-linear space and of ARIMA in linear space.
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In this paper, the Box-Jenkins modelling procedure is used to determine an ARIMA model and go further to forecasting. The mobile cellular subscription data for the study were taken from the administrative data submitted to the Zambia Information and Communications Technology Authority (ZICTA) as quarterly returns by all three mobile network operators Airtel Zambia, MTN Zambia and Zamtel. The time series of annual figures for mobile cellular subscription for all mobile network operators is from 2000 to 2014 and has a total of 15 observations. Results show that the ARIMA (1, 2, 1) is an adequate model which best fits the mobile cellular subscription time series and is therefore suitable for forecasting subscription. The model predicts a gradual rise in mobile cellular subscription in the next 5 years, culminating to about 9.0% cumulative increase in 2019.
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Stock market prediction has long been an area of interest for investors, traders, and researchers alike. Accurate forecasting of stock prices is crucial for financial decision-making and risk management. This paper presents a novel approach to predict stock prices by integrating Autoregressive Integrated Moving Average (ARIMA) and Exponential smoothing and Machine Learning (ML) techniques. Our study aims to enhance the predictive accuracy of stock price forecasting, which can significantly impact investment strategies and economic growth in this research paper implement the ARIMAML proposed method to predict the stock prices for Investment Bank of Iraq.
文摘We use the Autoregressive Integrated Moving Average(ARIMA)model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models’predictions.We first examine the stationary of the dataset and use ARIMA(0,1,1)to make predictions about the stock price during the pandemic,then we train the Prophet model using the stock price before January 1,2021,and predict the stock price after January 1,2021,to present.We also make a comparison of the prediction graphs of the two models.The empirical results show that the ARIMA model has a better performance in predicting Google’s stock price during the pandemic.
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funded by National Key R&D Program of China(No.2022YFC3003403)Sichuan Science and Technology Program(No.2024NSFSC0072)+1 种基金Natural Science Foundation of Hebei Province(No.F2021201031)Geological Survey Project of China Geological Survey(No.DD20230442).
文摘Infrasound,known for its strong penetration and low attenuation,is extensively used in monitoring and warning systems for debris flows.Here,a debris-flow forecasting method was proposed by combining infrasound-based variational mode decomposition and Autoregressive Integrated Moving Average(ARIMA)model.High-precision infrasound sensor was utilized in experiments to record signals under twelve varying conditions of debris flow volume and velocity.Variational mode decomposition was performed on the detected raw signals,and the optimal decomposition scale and penalty factor were obtained through the sparrow search algorithm.The Hilbert transform,rescaled range analysis,power spectrum analysis,and Pearson correlation coefficients judgment criteria were employed to separate and reconstruct the signals.Based on the reconstructed infrasound signals,an ARIMA model was constructed to forecast the trend of debris flow infrasound signal.Results reveal that the Hilbert transform effectively separated noise,and the predictive model’s results fell within a 95%confidence interval.The Mean Absolute Percentage Error(MAPE)across four experiments were 4.87%,5.23%,5.32%and 4.47%,respectively,showing a satisfactory accuracy and providing an alternative for predicting debris flow by infrasound signals.
文摘Background & Objectives: Sustainable Development Goals (SDGs) are set up as a part of the Post Millennium Development Goals (MDGs). Then it becomes essential to review the achievement of the MDGs in India and lessons learned to incorporate into the SDGs. The present study reviews and predicts different components of under-five mortality rate beyond 2015 to assess the present situation and to determine the future possibilities of achieving the new targets for SDGs in India. Data and Methods: It uses available time series data on different components of U5MR from the India’s Sample Registration System (SRS). Autoregressive Integrated Moving Averages (ARIMA) model has been taken as the method of time series analysis to forecast the mortality rates beyond 2015. Results: There is a consistent pattern of faster decline in the under-five mortality compared with the neonatal mortality rate across all major states in India although neonatal mortality contributes largest share in under-five mortality. Again, share of neonatal death among under-five death is increasing steadily over the future projected years. This indicates very slow progress of reduction in neonatal mortality. Stimulating efforts with new intervention programmes will be needed to focus more on lowering neonatal mortality particularly in rural India.
基金Supported by State Council Special Fund for Pollution Sources Survey (WPXC2007C200)~~
文摘Change trend of Chinese urban residents＇ per capita food-nitrogen annual consumption from 1981 to 2007 was analyzed and predicted by using ARIMA time-series model in order to reveal the change of urban food-nitrogen consumption during the China＇s urbanization process.Results showed that after 1980s,the annual consumption of Chinese urban residents＇ food-nitrogen had a change trend of ＂ increase-decrease-increase＂ and generally presented as a slight increasing trend;With the acceleration of rapid economic development and urbanization process,Chinese urban residents＇ food-nitrogen consumption will still keep a rising trend in future,and also has a large rising space.
文摘Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model’s parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1st to July7th 2010.
基金supported by the Fulbright-Nehru Doctoral Research program(Award No.2447/DR/2019-2020).
文摘This study focuses on the Indian gold futures market where primary participants hold sentimental value for the underlying asset and are globally ranked number two in terms of the largest private holdings in the physical form.The trade of gold futures relates to seasons,festivity,and government policy.So,the paper will discuss seasonality and intervention in the analysis.Due to non-constant variance,we will also use the standard variance stabilization transformation method and the ARIMA/GARCH modelling method to compare the forecast performance on the gold futures prices.The results from the analysis show that while the standard variance transformation method may provide better point forecast values,the ARIMA/GARCH modelling method provides much shorter forecast intervals.The empirical results of this study which rationalise the effect of seasonality in the Indian bullion derivative market have not been reported in literature.
文摘Quantifying the potential market of sports licenses is key in order for National Governing Bodies of sport （NGBs） to be able to design good strategic planning. We compared the classical methods of univariate prediction and the Autoregressive Integrated Moving Average （ARIMA） methods. Reliability of the available data was verified with the Time Series Regression with ARIMA Noise, Missing and Outliers （TRAMO） method, and the existence of a trend was verified using Daniel＇s test. For the purposes of this study--the researches collected and analysed secondary data from a 40-year series in 45 sports in Spain covering a very long period of time in a variety of sport disciplines. The study shows that, with the available data, short- and mid-term forecasting is possible in a number of sports, but not in all of them. It also proves that Holt＇s classical method of exponential smoothing is the one that yields best results. Golf, Basketball, Athletics and Hunting NGB show worrying prospects of decline levels and need an immediate change in the strategic plans. Other than for forecasting the evolution of athletes in the mid-term in order to improve strategic planning in NGBs, the present findings can be useful for public authorities to define their aid policies for NGBs, and they can also help companies in the industry to anticipate market developments.
文摘Data Mining has become an important technique for the exploration and extraction of data in numerous and various research projects in different fields (technology, information technology, business, the environment, economics, etc.). In the context of the analysis and visualisation of large amounts of data extracted using Data Mining on a temporary basis (time-series), free software such as R has appeared in the international context as a perfect inexpensive and efficient tool of exploitation and visualisation of time series. This has allowed the development of models, which help to extract the most relevant information from large volumes of data. In this regard, a script has been developed with the goal of implementing ARIMA models, showing these as useful and quick mechanisms for the extraction, analysis and visualisation of large data volumes, in addition to presenting the great advantage of being applied in multiple branches of knowledge from economy, demography, physics, mathematics and fisheries among others. Therefore, ARIMA models appear as a Data Mining technique, offering reliable, robust and high-quality results, to help validate and sustain the research carried out.
基金The National Natural Science Foundation of China(No.61273236)the Natural Science Foundation of Jiangsu Province(No.BK2010239)the Ph.D.Programs Foundation of Ministry of Education of China(No.200802861061)
文摘This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.
