Stocks & Commodities V. 23:11 (44-48): Singular Spectrum Analysis: Part II by Sergiy Drogobetskii
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Stocks & Commodities V. 23:11 (44-48): Singular Spectrum Analysis: Part II by Sergiy Drogobetskii
Here’s another look at applying singular spectrum analysis to the foreign exchange markets.
In my last article, I discussed some of the variables that make up single spectrum analysis (SSA), an analytical method that has been applied to various branches of scientific study. It has proved useful for compressing information, smoothing initial data and, in certain cases, predicting time series data prices. Last time, I showed how SSA could be applied to forex market prices. This time, I will continue in that direction and show how you can apply SSA to forecast prices. In reconstructing the initial time series, one important consequence of the singular value decomposition (SVD) of a trajectory matrix (built from the time series of prices) is that it is possible to completely reconstruct the decomposed initial trajectory matrix by using empirical orthogonal functions (EOFs) contained in the right matrix V...
Here’s another look at applying singular spectrum analysis to the foreign exchange markets.
In my last article, I discussed some of the variables that make up single spectrum analysis (SSA), an analytical method that has been applied to various branches of scientific study. It has proved useful for compressing information, smoothing initial data and, in certain cases, predicting time series data prices. Last time, I showed how SSA could be applied to forex market prices. This time, I will continue in that direction and show how you can apply SSA to forecast prices. In reconstructing the initial time series, one important consequence of the singular value decomposition (SVD) of a trajectory matrix (built from the time series of prices) is that it is possible to completely reconstruct the decomposed initial trajectory matrix by using empirical orthogonal functions (EOFs) contained in the right matrix V...
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