计算机与现代化
計算機與現代化
계산궤여현대화
COMPUTER AND MODERNIZATION
2014年
11期
66-71
,共6页
预测算法%时间序列%小波分析%马尔科夫模型%指数平滑法
預測算法%時間序列%小波分析%馬爾科伕模型%指數平滑法
예측산법%시간서렬%소파분석%마이과부모형%지수평활법
prediction algorithm%time series%wavelet analysis%Markov model%smoothing coefficient
时间序列的传统预测方法能够很好地拟合和预测平稳时间序列，对于非线性非平稳的时间序列数据预测效果不好。为解决该问题，文本提出一种改进的预测算法。通过小波分解和单边重构，原始时间序列被分解为一列低频数据和两列高频数据。低频数据采用传统的时间序列方法GARCH模型预测，高频数据使用改进方法预测。通过马尔科夫模型预测出状态区间，结合指数平滑法，预测出高频结果。与低频数据结果叠加得到最终预测结果。经误差比较，改进算法预测精度有较大提升。
時間序列的傳統預測方法能夠很好地擬閤和預測平穩時間序列，對于非線性非平穩的時間序列數據預測效果不好。為解決該問題，文本提齣一種改進的預測算法。通過小波分解和單邊重構，原始時間序列被分解為一列低頻數據和兩列高頻數據。低頻數據採用傳統的時間序列方法GARCH模型預測，高頻數據使用改進方法預測。通過馬爾科伕模型預測齣狀態區間，結閤指數平滑法，預測齣高頻結果。與低頻數據結果疊加得到最終預測結果。經誤差比較，改進算法預測精度有較大提升。
시간서렬적전통예측방법능구흔호지의합화예측평은시간서렬，대우비선성비평은적시간서렬수거예측효과불호。위해결해문제，문본제출일충개진적예측산법。통과소파분해화단변중구，원시시간서렬피분해위일렬저빈수거화량렬고빈수거。저빈수거채용전통적시간서렬방법GARCH모형예측，고빈수거사용개진방법예측。통과마이과부모형예측출상태구간，결합지수평활법，예측출고빈결과。여저빈수거결과첩가득도최종예측결과。경오차비교，개진산법예측정도유교대제승。
The traditional time series prediction algorithm can well simulate and predict the stable time series data, but not so well to the series of nonlinear and non-stationary. To solve this problem, an improved algorithm comes up. Through the wavelet decomposition and single reconstruction, the original time series is decomposed into a layer of low frequency data and two layers of high frequency data. The GARCH model is used to forcast the low frequency data, the improved algorithm is used to forecast the two layers of high frequency data. Through Markov model predicting the state interval, with the smoothing coefficient, the high frequency data is predicted. The final forecasting result comes from the superposition of the three layers of prediction result. Through the error test, the accuracy of the improved algorithm has a major improvement.
