This paper explores the capabilities of machine learning for probabilistic forecasting fractional Brownian motion (fBm). The focus is on predicting probability value an fBm time series exceeding a certain threshold after specific number steps, given only knowledge its Hurst exponent. study aims to determine if self-similarity property preserved in and which algorithms are most effective. Two ty...

The periodogram is a widely used tool to analyze second order stationary time series. An attractive feature of the that expectation approximately equal underlying spectral density However, this only an approximation, and it well known has finite sample bias, which can be severe in small samples. In article, we show bias arises because boundary observation one discrete Fourier transforms constru...

Accurately predicting the trend of stock return rate is a hot research issue. With development artificial intelligence, machine learning, big data and other technologies, it brings new potential to prediction market. In order accurately predict return, this paper mainly constructs time series ARMA model random forest model, uses stacking method fuse models, predicts daily Yangtze River Electric...