Stocks & Commodities V. 23:10 (24-27): Singular Spectrum Analysis Of Price Movement In Forex by Sergiy Drogobetskii
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Stocks & Commodities V. 23:10 (24-27): Singular Spectrum Analysis Of Price Movement In Forex by Sergiy Drogobetskii
Looking for new information about price movements? Singular spectrum analysis may shed some light.
Finding an analytical method to reduce noise and predict price movement dynamics has been a popular topic of discussion among traders. In fact, several methods from the fields of physics and mathematics have already been applied for this very reason. But how do you extract the necessary information, and what can you use as the basis for your forecasting model?
SINGULAR SPECTRUM ANALYSIS
Although there are several methods for analyzing time series, they tend to have limitations. The method I will discuss in this article has some advantages over other time series analysis techniques: It can tell you how to extract relevant information from noisy time series and what to use as a basis for your forecasting model. Singular spectrum analysis (SSA) is a new analytical method that has been applied to branches of scientific study such as bioinformatics, meteorology, astronomy, and pattern recognition. SSA is useful for compressing information, smoothing of initial data and, in certain cases, predicting time series data prices. In this article, I will apply SSA to forex market prices.
Like other financial markets, the forex market is a complex, dynamic system. Based on my analysis of economic systems, I thought it best to apply a passive experiment that involves observing the behavior of a system over time. This resulted in representing the values of observable magnitudes as a time series. The SSA was designed to provide insight into the dynamics of the process that generates time series. It is based on the singular value decomposition (SVD) of a trajectory matrix that is constructed from the time series of prices.
Looking for new information about price movements? Singular spectrum analysis may shed some light.
Finding an analytical method to reduce noise and predict price movement dynamics has been a popular topic of discussion among traders. In fact, several methods from the fields of physics and mathematics have already been applied for this very reason. But how do you extract the necessary information, and what can you use as the basis for your forecasting model?
SINGULAR SPECTRUM ANALYSIS
Although there are several methods for analyzing time series, they tend to have limitations. The method I will discuss in this article has some advantages over other time series analysis techniques: It can tell you how to extract relevant information from noisy time series and what to use as a basis for your forecasting model. Singular spectrum analysis (SSA) is a new analytical method that has been applied to branches of scientific study such as bioinformatics, meteorology, astronomy, and pattern recognition. SSA is useful for compressing information, smoothing of initial data and, in certain cases, predicting time series data prices. In this article, I will apply SSA to forex market prices.
Like other financial markets, the forex market is a complex, dynamic system. Based on my analysis of economic systems, I thought it best to apply a passive experiment that involves observing the behavior of a system over time. This resulted in representing the values of observable magnitudes as a time series. The SSA was designed to provide insight into the dynamics of the process that generates time series. It is based on the singular value decomposition (SVD) of a trajectory matrix that is constructed from the time series of prices.
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