Due to the advance of storage technology in computers, matrix valued time series observations are collecting in many fields like finance, economics, weather forecasting, and many fields. The matrix valued data bring information along its row wise and column wise, and if they are collect over the time we have to treat the data as a time series. If we use classical dimension reduction by converting the matrix into a long vector, the structural information is lost. Here, we discuss two different ways of dimension reduction namely: PCA model for matrix valued time series data and the FA model for matrix valued time series data which keep the matrix structure and reduce the dimension of the observed data. The estimating procedure, theoretical properties and a simulation study are presented and demonstrated for both of these models.
| Identifer | oai:union.ndltd.org:siu.edu/oai:opensiuc.lib.siu.edu:theses-3211 |
| Date | 01 August 2017 |
| Creators | TharinduPriyanDeAlwis, MahappuKankanamge |
| Publisher | OpenSIUC |
| Source Sets | Southern Illinois University Carbondale |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses |
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