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Search Results: 1 - 10 of 1965 matches for " Seasonal ARIMA "
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A Multiplicative Seasonal ARIMA/GARCH Model in EVN Traffic Prediction  [PDF]
Quang Thanh Tran, Zhihua Ma, Hengchao Li, Li Hao, Quang Khai Trinh
Int'l J. of Communications, Network and System Sciences (IJCNS) , 2015, DOI: 10.4236/ijcns.2015.84005
Abstract:

This paper highlights the statistical procedure used in developing models that have the ability of capturing and forecasting the traffic of mobile communication network operating in Vietnam. To build such models, we follow Box-Jenkins method to construct a multiplicative seasonal ARIMA model to represent the mean component using the past values of traffic, then incorporate a GARCH model to represent its volatility. The traffic is collected from EVN Telecom mobile communication network. Diagnostic tests and examination of forecast accuracy measures indicate that the multiplicative seasonal ARIMA/GARCH model, i.e. ARIMA (1, 0, 1) × (0, 1, 1)24/GARCH (1, 1) shows a good estimation when dealing with volatility clustering in the data series. This model can be considered to be a flexible model to capture well the characteristics of EVN traffic series and give reasonable forecasting results. Moreover, in such situations that the volatility is not necessary to be taken into account, i.e. short-term prediction, the multiplicative seasonal ARIMA/GARCH model still acts well with the GARCH parameters adjusted to GARCH (0, 0).

云南省CPI序列的分析与预测—基于SARIMA模型
Analysis and Prediction of Yunnan CPI Series—Based on SARIMA Model
 [PDF]

李卓然, 孙晓宇
Statistics and Applications (SA) , 2016, DOI: 10.12677/SA.2016.52015
Abstract: 本文以云南省为例,运用近20年来的月度数据对CPI进行建模预测。分析表明,CPI数据呈现周期为12的季节性;文章通过建立季节性ARIMA模型,预测2016年第二季度云南省CPI将在第一季度的基础上逐渐上升,且能够保持在稳定增长的范围内。
Taking Yunnan Province as an example, monthly data nearly 20 years were used on CPI forecast modeling. Analysis shows that the CPI data present seasonal cycle of 12. Through the establishment of the seasonal ARIMA models in this article, we predict the CPI of Yunnan Province in 2016 in the second quarter will gradually rise on the basis of the first quarter. And it is able to maintain within the scope of stable growth.
Prediction of Civil Aviation Passenger Transportation Based on ARIMA Model  [PDF]
Xinxin Tang, Guangming Deng
Open Journal of Statistics (OJS) , 2016, DOI: 10.4236/ojs.2016.65068
Abstract: 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.
基于ARIMA乘积季节模型的港口集装箱吞吐量预测
Port Container Throughput Forecasting Based on the Multiplicative Seasonal ARIMA Model
 [PDF]

陶丽丽, 王艳
Operations Research and Fuzziology (ORF) , 2015, DOI: 10.12677/ORF.2015.52005
Abstract:
在对时间序列分析理论研究基础上,利用MATLAB软件编写所有算法的程序系统地分析港口集装箱吞吐量月度数据的变化规律,建立的ARIMA乘积季节模型能充分反映港口集装箱吞吐量的时间序列变化规律。以上海港2002~2009年集装箱吞吐量为例,应用MATLAB软件建立了ARIMA(0,1,1)×(0,1,1)12乘积季节模型,结果表明该乘积季节模型的预测精度较高,预测结果更加合理,有着广泛的应用前景。
Based on the theoretical research of the time series analysis, this paper systematically analyzes the changes rules of the monthly data of container throughput of Shanghai Port from 2002 to 2009 by using MATLAB software. The result shows that the multiplicative seasonal ARIMA(0,1,1(0,1,1)12 model has a high forecasting precision, a reasonable forecasting result and a broad application prospect.
Comportamiento estacional de la mortalidad infantil en Cuba, 1987-2004
Coutin Marie,Gisele; Zambrano Cárdenas,Andrés;
Revista Cubana de Higiene y Epidemiolog?-a , 2006,
Abstract: mortality presents a variable seasonal performance in almost every region of the world, although their patterns change a lot with the different territories and the causes of death. it has been associated with the variations resulting from climate changes and with several environmental factors. a number of studies has described a high number of deaths in winter time, mainly due to respiratory diseases, acute myocardial infarct and cerebrovascular diseases whereas the increase of the mortality rate from diarrheal illnesses is more common in summer time. since 19th century, the seasonal variation of mortality is known in cuba and from that date on, excessive mortality rates in the warmest months, most of deaths occurring in the elderly and small children, have been observed; however, there are so far no studies that deepen into this issue. this paper presented the results of a descriptive research work through time series analysis techniques in order to identify and describe the seasonal performance of infant mortality and of some selected causes of death in the 1987-2005 period; and also to obtain forecasts for the year 2006. among the most striking results were the detection of seasonal variation in the infant mortality series of the summer time, mainly in july, and a variable seasonal performance for the various causes of death.
Seasonal Arima Modelling of Nigerian Monthly Crude Oil Prices
Ette Harrison Etuk
Asian Economic and Financial Review , 2013,
Abstract: The time plot of the series NCOP reveals a peak in 2008 and a depression in early 2009. The overall trend is horizontal and no seasonality is obvious. Twelve-month differencing yields SDNCOP exhibiting still a peak in 2008 and a trough in 2009, the overall trend being slightly positive and seasonality not easily discernible. Nonseasonal differencing of SDNCOP yields DSDNCOP with an overall horizontal trend and no obvious seasonality. However its correlogram reveals an autocorrelation structure of a seasonal model of order 12. Moreover it suggests the product of two moving average components both of order one, one non-seasonal and the other 12-month seasonal. The partial autocorrelation function suggests the involvement of a seasonal (i.e. 12-month) autoregressive component of order one. A (0, 1, 1)x(1, 1, 1)12 autoregressive integrated moving average model was therefore proposed and fitted. It has been shown to be adequate.
EXTRACTION OF SEASONAL VARIATIONS OF UNEMPLOYMENT RATE IN ROMANIA USING SEVERAL METHODS BASED ON MOVING AVERAGE FILTER
Mariana GAGEA,Alina M?riuca IONESCU
Scientific Annals of the Alexandru Ioan Cuza University of Iasi : Economic Sciences Series , 2008,
Abstract: At present, both at European Union and world level, experts are preoccupied to find the best method for the deseasonalisation of a time series that should assure the comparability of statistical data. The present paper follows the line of these researches.In the study, we undertake a comparison of the most representative methods based on moving average filter: moving average method, Census X-11 method and X-12 ARIMA method. Theoretical research shows the superiority of X-12 ARIMA method, which has incorporated the previous methods as regards the algorithm and the advantages, contributing to the improvement of the weaknesses of the former methods. The criteria for the comparison of the results obtained through the three methods applied to the time series of unemployment rate in Romania during the period 2000 - 2007 didn’t indicate a unique method, as being the most adequate for deseasonalisation.
The Application of the Structure Time Series Model on Seasonal Adjustment——Compared with X-12 Seasonal Adjustment Method
结构时间序列模型在季节调整方面的应用——与X-12季节调整方法的比较分析

CHEN Fei,GAO Tie-mei,
陈 飞
,高铁梅

系统工程理论与实践 , 2007,
Abstract: In the paper,we construct a new seasonal adjustment method of time series on the basis of the structural time series model.By researching the structure of economic time series using ARIMA model,we firstly establish the expression of trend-cycle component according to the order of integration(d),and set up different forms of structural time series models.In the structure time series model,the economic indicator is decomposed into trend,cycle,seasonal and irregular components,which are unobserved and thus can't be estimated by classical regression way.So we estimate the model in the form of state space.Further,we use the model to decompose China's economic time series,such as GDP,Total Retail Sales of Consumer Goods,etc.Moreover we compare our model's results with X-12 seasonal adjustment method's,and the empirical conclusions show that the structure time series model is more stable when it is used to decompose seasonal component.
Comportamiento estacional de la mortalidad infantil en Cuba, 1987-2004 Seasonal performance of infant mortality in Cuba, 1987- 2004
Gisele Coutin Marie,Andrés Zambrano Cárdenas
Revista Cubana de Higiene y Epidemiología , 2006,
Abstract: La mortalidad presenta un comportamiento estacional en casi todas las regiones del mundo, aunque sus patrones varían mucho según los territorios y las causas de muerte. Ha sido asociada a las variaciones resultantes de los cambios climáticos y también a diferentes factores ecológicos. Numerosos estudios han descrito un elevado número de defunciones en el invierno, sobre todo por enfermedades respiratorias, infarto agudo del miocardio y enfermedades cerebrovasculares, mientras que en el verano ha sido más común el incremento de la mortalidad por enfermedades diarreicas. En Cuba se reconoce la estacionalidad de la mortalidad desde el siglo XIX, y desde entonces se ha identificado un exceso de mortalidad durante los meses más cálidos, así como la concentración de las defunciones en los ancianos y ni os peque os; sin embargo, no existen estudios recientes que profundicen en este aspecto. En el presente trabajo se muestran los resultados de una investigación descriptiva mediante las técnicas de análisis de series temporales para identificar y describir la estacionalidad de la mortalidad infantil y de algunas causas de muerte seleccionadas para el período 1987-2005, así como para la obtención de pronósticos en los meses del a o 2006. Entre los resultados más importantes están la detección de la presencia de estacionalidad en la serie de mortalidad infantil durante los meses del verano, sobre todo en julio, y un comportamiento estacional variable para las distintas causas de muerte. Mortality presents a variable seasonal performance in almost every region of the world, although their patterns change a lot with the different territories and the causes of death. It has been associated with the variations resulting from climate changes and with several environmental factors. A number of studies has described a high number of deaths in winter time, mainly due to respiratory diseases, acute myocardial infarct and cerebrovascular diseases whereas the increase of the mortality rate from diarrheal illnesses is more common in summer time. Since 19th century, the seasonal variation of mortality is known in Cuba and from that date on, excessive mortality rates in the warmest months, most of deaths occurring in the elderly and small children, have been observed; however, there are so far no studies that deepen into this issue. This paper presented the results of a descriptive research work through time series analysis techniques in order to identify and describe the seasonal performance of infant mortality and of some selected causes of death in the 1987-2005 period; and
基于乘积SARIMA模型的肺结核发病率预测
胡晓媛,孙庆文,王玲玲,李敏
- , 2016, DOI: 10.16781/j.0258-879x.2016.08.0969
Abstract: 目的 应用乘积季节自回归移动平均(seasonal autoregressive integrated moving average,SARIMA)模型对肺结核发病率进行预测研究,探讨其可行性并为肺结核病的防治工作提供科学依据。方法 应用EViews 7.0.0.1软件对我国2004年1月至2012年12月的肺结核逐月发病率建立乘积SARIMA模型并进行拟合,选取2013年1月至12月肺结核发病率数据评价模型的预测性能。结果 建立的SARIMA(2,0,2)×(0,1,1)12模型能较好地拟合既往时间段内肺结核的发病率,对2013年1月至12月肺结核发病率的预测与实际发病率趋势基本吻合,平均误差绝对值为0.416 992,平均误差绝对率为5.350 8%。结论 乘积SARIMA模型能较好地模拟和预测肺结核发病率在时间序列上的变动趋势,将其应用于肺结核发病预测是可行的,具有推广应用前景。
Objective To examine the feasibility of using multiple seasonal autoregressive integrated moving average (SARIMA) model for predicting pulmonary tuberculosis (TB) incidence, so as to provide scientific evidence for the prevention and treatment of TB. Methods EViews 7.0.0.1 software was used to create a SARIMA fit model for seasonal incidence of TB on a monthly basis from January 2004 to December 2012, and the predicting performance of the model was tested with TB data from January to December in 2013. Results The established SARIMA (2,0,2)×(0,1,1)12 model could better fit with the previous TB incidence; and it basically well predicted the TB incidence of the 12 months of 2013, with the mean absolute error being 0.416 992 and the mean absolute error rate being 5.350 8%. Conclusion The established multiplicative SARIMA model can better simulate and predict the trend of TB incidence with time, and it may have a future in predicting the incidence of TB
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