Stocks & Commodities V. 26:7 (38-40): Forecasting Singular Spectrum Analysis by S. Drogobetskii and V. Smolynsky
Product Description
Forecasting Singular Spectrum Analysis by S. Drogobetskii and V. Smolynsky
Find components in the markets such as trends and random noise that can help you analyze and forecast the financial markets.
Applying mathematical statistics to analyze and forecast the commodity and financial markets is nothing new. Traders have been applying classical models of trend plus noise or autoregression (moving average) and come up with satisfactory results. But those models tend to be simple in structure. The peculiarity of market behavior is that its characteristics (price, amount of transactions, indicators, and so forth) are made up of several components: a slow component, which is the trend; periodical, or oscillatory; and a random component described by the randomness of the price series. The periodical component is made up of periodicity with variable period and amplitude.
The classic methods of analysis such as Fourier analysis, regression analysis, or wavelet analysis tend to decompose the initial function into a series using a fixed system of basic functions such as sines and cosines, which produce strong periodicity property. In this article, I will discuss singular spectrum analysis (SSA), which is data-adaptive and a nonparametric method.
Find components in the markets such as trends and random noise that can help you analyze and forecast the financial markets.
Applying mathematical statistics to analyze and forecast the commodity and financial markets is nothing new. Traders have been applying classical models of trend plus noise or autoregression (moving average) and come up with satisfactory results. But those models tend to be simple in structure. The peculiarity of market behavior is that its characteristics (price, amount of transactions, indicators, and so forth) are made up of several components: a slow component, which is the trend; periodical, or oscillatory; and a random component described by the randomness of the price series. The periodical component is made up of periodicity with variable period and amplitude.
The classic methods of analysis such as Fourier analysis, regression analysis, or wavelet analysis tend to decompose the initial function into a series using a fixed system of basic functions such as sines and cosines, which produce strong periodicity property. In this article, I will discuss singular spectrum analysis (SSA), which is data-adaptive and a nonparametric method.
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