Comparative Study of Time Series Algorithms on Stock Price Forecasting

Abstract

Gauging the pulse of political climes and global tides alike, the stock exchange carries considerable consequence. Given the capricious manner by which the apparatus maneuvers, present technological means and trade tenets forbid accurately foretelling how planetary perturbations may sway share values over extended time. Of late, nonetheless, informatic techniques and artificially adroit algorithms have enabled scrutinizing episodes and occurrences that surface symbiotically, prognosticating the trajectory of equity prices for most, if not all, corporations in qualifying bourses. Past the fluctuating cultural zeitgeists and mercantile vicissitudes defining our epoch's global political topography, prognosticating distant futures proves an elusive enterprise. Our objective henceforth shall be to prognosticate the quotidian inclination of equity premiums in the stock bazaar with the utmost precision achievable. The Stacking Ensemble model is a prevalent and trustworthy fashion to anticipate chronological successions. We shall explain why we opt for it over and above the other techniques adduced, akin to Meta Prophet, and the LSTM modus operandi, posterior to assaying. We manipulate a combination exemplar to ascertain the weighted norm of all in- sample data. In this exploration, we endeavored to disparate things with the stock valuations of multiple shareholding fully remunerated ordinary portions and thereafter effectuated predictions about stock valuations.