Data is generated by using Quasiperiodic function and additive noise
Polynomial regression model for a single predictor, X, is:
where h is called the degree of the polynomial. For lower degrees, the relationship has a specific name (i.e., h = 2 is called quadratic, h = 3 is called cubic, h = 4 is called quartic, and so on). Although this model allows for a nonlinear relationship between Y and X, polynomial regression is still considered linear regression since it is linear in the regression coefficients β1, β2,..., βh!
Lineer Regression Assumption: data = model + noise
Noise is a kind of gaussian or normal distribution so we need to check the residual by using the normality test (Shapiro-Wilk) but first let's look at trend.
The main idea is to extract the different modes of a signal by designing an appropriate wavelet filter bank. This construction leads us to build adaptive wavelets called the empirical wavelet transform.
https://ieeexplore.ieee.org/document/6522142
https://github.com/HarishBachu/StockPrediction
In time series analysis, singular spectrum analysis (SSA) is a nonparametric spectral estimation method. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing.
https://en.wikipedia.org/wiki/Singular_spectrum_analysis
https://ui.adsabs.harvard.edu/abs/2019GeoJI.217..748P/abstract
https://github.com/dmarienko/chaos/blob/master/SSA_for_stock_prices_prediction.ipynb
PEP-8 coding style is used for the codes.