GREAT SALT LAKE SURFACE LEVEL FORECASTING USING FIGARCH MODELING

机译：利用FIGARCH建模预测大盐湖地表水准

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In this paper, we have examined 4 models for Great Salt Lake level forecasting: ARMA (Auto-Regression and Moving Average), ARFIMA (Auto-Regressive Fractional Integral and Moving Average), GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) and FIGARCH (Fractional Integral Generalized Auto-Regressive Conditional Heteroskedasticity). Through our empirical data analysis where we divide the time series in two parts (first 2000 measurement points in Part-1 and the rest is Part-2), we found that for Part-2 data, FIGARCH offers best performance indicating that conditional heteroscedasticity should be included in time series with high volatility.

机译：在本文中，我们检查了4种用于大盐湖水位预测的模型：ARMA（自回归和移动平均），ARFIMA（自回归分数积分和移动平均），GARCH（广义自回归条件异方差）和FIGARCH（分数积分广义自回归条件异方差。通过我们的经验数据分析，我们将时间序列分为两部分（第1部分中第一个2000个测量点，其余部分为第2部分），我们发现对于第2部分数据，FIGARCH提供了最佳性能，表明条件异方差应该包含在高波动性的时间序列中。

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《ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 2007》|2007年|P.13611370|共2页
会议地点 Las VegasNV(US);Las VegasNV(US);Las VegasNV(US)
作者
Qianru Li; Christophe Tricaud; Rongtao Sun; YangQuan Chen;
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Department of Economics 3530 Old Main Hill Logan, Utah State University, Logan, UT 84322-3530, USA;

Center for Self-Organizing and Intelligent Systems (CSOIS) Dept. of Electrical and Computer Engineering 4160 Old Main Hill, Utah State University, Loqan, U;

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GREAT SALT LAKE SURFACE LEVEL FORECASTING USING FIGARCH MODELING

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