Signal processing applied in cortex project: From noise analysis to OMA and SSA methods

Montalvo, C.; Pantera, Laurent; Lipcsei, S.; Barrio-Parra, Fernando

doi:10.1016/j.anucene.2022.109193

Cited by 2 publications

(2 citation statements)

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“…As is the case with the Fourier transform, SSA decomposes the original time series into a sum of components. However, in the Fourier analysis, the time series (the summands) are periodic functions of different frequency and amplitude, while SSA decomposes the original series into trending, periodic and noise elements [8,9]. Thus, SSA takes into account such peculiarities of the financial time series as non-stationarity and non-differentiability.…”

Section: Methodsmentioning

confidence: 99%

“…To do ARIMA forecasting in Python, it is sufficient to determine the parameters p, d, q of the model. Formally, the ARIMA model is described as follows: (9) where is the difference of d-order required to achieve stationarity; are the p-order autoregression coefficients; β j are the q-order moving average coefficients; are the moving average forecasting errors.…”

Section: Methodsmentioning

confidence: 99%

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Forecasting financial time series using singular spectrum analysis

Zinenko

2023

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Financial time series are big arrays of information on quotes and trading volumes of shares, currencies and other exchange and over-the-counter instruments. The analysis and forecasting of such series has always been of particular interest for both research analysts and practicing investors. However, financial time series have their own features, which do not allow one to choose the only correct and well-functioning forecasting method. Currently, machine-learning algorithms allow one to analyze large amounts of data and test the resulting models. Modern technologies enable testing and applying complex forecasting methods that require volumetric calculations. They make it possible to develop the mathematical basis of forecasting, to combine different approaches into a single method. An example of such a modern approach is the Singular Spectrum Analysis (SSA), which combines the decomposition of a time series into a sum of time series, principal component analysis and recurrent forecasting. The purpose of this work is to analyze the possibility of applying SSA to financial time series. The SSA method was considered in comparison with other common methods for forecasting financial time series: ARIMA, Fourier transform and recurrent neural network. To implement the methods, a software algorithm in the Python language was developed. The method was also tested on the time series of quotes of Russian and American stocks, currencies and cryptocurrencies.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%