计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
2期
259-264
,共6页
民航货邮周转量%GM(1,1)模型%自回归移动平均(ARIMA)模型%组合模型
民航貨郵週轉量%GM(1,1)模型%自迴歸移動平均(ARIMA)模型%組閤模型
민항화유주전량%GM(1,1)모형%자회귀이동평균(ARIMA)모형%조합모형
civil aviation RFTK%GM(1,1)model%Autoregressive Integrated Moving Average(ARIMA)model%combi-nation model
ARIMA模型对季节特征有较好的拟合效果,灰色GM(1,1)模型能准确反映时间序列的增长趋势,结合民航货邮周转量的特点和ARIMA模型和GM(1,1)模型的优点,分别建立货邮周转量的ARIMA和GM(1,1)的时间序列模型,揭示出民航货邮周转量随时间推移而发展变化的动态规律,最后为更精确地预测月度民航货邮周转量,提出基于ARIMA-GM的组合预测模型,并对近几月民航货邮周转量进行较准确的短期预测,结果表明:组合模型能提高预测精度,在实际应用中ARIMA模型可用于非季节和季节的各类时间序列;灰色GM(1,1)模型能准确反映时间序列的增长趋势,两者相结合很好地解决了民航货邮周转量短期预测的实际问题,得到民航货邮周转量更精确的预测结论,能够对民航货邮市场的发展趋势进行宏观把握,有利于决策者的经济决策行为。
ARIMA模型對季節特徵有較好的擬閤效果,灰色GM(1,1)模型能準確反映時間序列的增長趨勢,結閤民航貨郵週轉量的特點和ARIMA模型和GM(1,1)模型的優點,分彆建立貨郵週轉量的ARIMA和GM(1,1)的時間序列模型,揭示齣民航貨郵週轉量隨時間推移而髮展變化的動態規律,最後為更精確地預測月度民航貨郵週轉量,提齣基于ARIMA-GM的組閤預測模型,併對近幾月民航貨郵週轉量進行較準確的短期預測,結果錶明:組閤模型能提高預測精度,在實際應用中ARIMA模型可用于非季節和季節的各類時間序列;灰色GM(1,1)模型能準確反映時間序列的增長趨勢,兩者相結閤很好地解決瞭民航貨郵週轉量短期預測的實際問題,得到民航貨郵週轉量更精確的預測結論,能夠對民航貨郵市場的髮展趨勢進行宏觀把握,有利于決策者的經濟決策行為。
ARIMA모형대계절특정유교호적의합효과,회색GM(1,1)모형능준학반영시간서렬적증장추세,결합민항화유주전량적특점화ARIMA모형화GM(1,1)모형적우점,분별건립화유주전량적ARIMA화GM(1,1)적시간서렬모형,게시출민항화유주전량수시간추이이발전변화적동태규률,최후위경정학지예측월도민항화유주전량,제출기우ARIMA-GM적조합예측모형,병대근궤월민항화유주전량진행교준학적단기예측,결과표명:조합모형능제고예측정도,재실제응용중ARIMA모형가용우비계절화계절적각류시간서렬;회색GM(1,1)모형능준학반영시간서렬적증장추세,량자상결합흔호지해결료민항화유주전량단기예측적실제문제,득도민항화유주전량경정학적예측결론,능구대민항화유시장적발전추세진행굉관파악,유리우결책자적경제결책행위。
ARIMA model has a better fitting effect on seasonal feature. Grey model can accurately reflect the growth trend of time series. Combined the characteristics of civil aviation RFTK with the advantages of ARIMA model and GM(1, 1) model, comprehensively using the analysis method of time series, this paper presents the combination prediction model of ARIMA-GM. It respectively establishes the time series model of ARIMA model and GM(1, 1)model to show the dynamic rule of civil aviation RFTK changing as time passing. At last, for more accurately predicting the month civil aviation RFTK, this paper puts forward the forecasting method of combination model, and makes an exact forecast of the civil avi-ation RFTK in a few months. The combination model is a good solution to the practical problems for civil aviation RFTK forecasting, based on which it can have a macro-grasp of civil aviation RFTK market trend, which will certainly be condu-cive to economic decision-making.