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.