專題演講 主講人：Prof. William W.S. Wei
(Department of Statistics, Operations and Data Science, Temple University Philadelphia, USA)
題 目：Dimension Reduction in High Dimensional Multivariate Time Series Analysis
主講人：Prof. William W.S. Wei
(Department of Statistics, Operations and Data Science, Temple University Philadelphia, USA)
時 間：111年6月17日(星期五)上午10:40-11:30
(上午10:20-10:40茶會於交大統計所428室舉行)
地 點：綜合一館427室
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摘要
The vector autoregressive (VAR) and vector autoregressive moving average (VARMA) models have been widely used to model multivariate time series, because of their ability to represent the dynamic relationships among variables in a system and their usefulness in forecasting unknown future values. However, when the number of dimensions is very large, the number of parameters often exceeds the number of available observations, and it is impossible to estimate the parameters. A suitable solution is clearly needed. In this paper, after introducing some existing methods, we will suggest the use of contemporal aggregation as a dimension reduction method, which is very natural and simple to use. We will compare our proposed method with other existing methods in terms of forecast accuracy through both simulations and empirical examples.
