A Multiscale Statistical Model for Time Series Forecasting

机译：时间序列预测的多尺度统计模型

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We propose a stochastic grammar model for random-walk-like time series that has features at several temporal scales. We use a tree structure to model these multiscale features. The inside-outside algorithm is used to estimate the model parameters. We develop an algorithm to forecast the sign of the first difference of a time series. We illustrate the algorithm using log-price series of several stocks and compare with linear prediction and a neural network approach. We furthermore illustrate our algorithm using synthetic data and show that it significantly outperforms both the linear predictor and the neural network. The construction of our synthetic data indicates what types of signals our algorithm is well suited for.

机译：我们为类似随机游走的时间序列提出了一种随机语法模型，该模型具有多个时间尺度。我们使用树结构为这些多尺度特征建模。内外算法用于估计模型参数。我们开发了一种算法来预测时间序列的第一个差异的符号。我们使用几只股票的对数-价格序列来说明该算法，并与线性预测和神经网络方法进行比较。我们还使用合成数据说明了我们的算法，并表明该算法明显优于线性预测器和神经网络。综合数据的构建表明我们的算法非常适合哪种信号。

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来源
《Computational Imaging V; Proceedings of SPIE-The International Society for Optical Engineering; vol.6498; Electronic Imaging Science and Technology》|2007年|649815.1-649815.9|共9页
会议地点 San JoseCA(US)
作者
W. Wang; I. Pollak;
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作者单位

Purdue University, West Lafayette, IN 47907;

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原文格式 PDF
正文语种 eng
中图分类图像信号处理;
关键词
stochastic context-free grammar; EM algorithm; graphical model; inside-outside algorithm; forecasting;

机译：随机上下文无关语法； EM算法；图形模型内外算法；预测;

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A Multiscale Statistical Model for Time Series Forecasting

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