Time series analysis and financial asset forecasting methods

The article discusses the principal features of time series. Time series analysis makes it possible to identify trends, cycles, and seasonal variances to aid in the forecasting of a future event. Statistical forecasting methods and machine learning-based methods are described. Neural network models are widely used in stock market forecasting because of their inherent ability to identify complex nonlinear relationships present in given time series and to accurately approximate any nonlinear function.