Stock price forecasting model analysis based on the wavelet neural network and the data cleaning technology

Abstract

Abstract. Financial time arrangement, for example, stock cost and trade rates are, regularly, non-straight and non-stationary. Beforehand, numerous scientists have endeavored to conjecture those utilizing factual models and machine learning models. Measurable models expect the time arrangement to be stationary and straight, accordingly bringing about expansive factual mistakes. Examination and expectation of securities exchange time arrangement information has pulled in impressive enthusiasm from the exploration group in the course of the most recent decade. Quick advancement and development of modern calculations for measurable examination of time arrangement information, and accessibility of elite equipment has made it conceivable to prepare and dissect high volume securities exchange time arrangement information successfully, continuously. In monetary field, exceptions speak to unpredictability of securities exchange, which assumes an imperative part in administration, portfolio choice and subordinate evaluating. Along these lines, anticipating anomalies of securities exchange is of the immense significance in principle and application. In this paper, the issue of anticipating anomalies in light of versatile gathering models of Intense Erudition Machines (IEMs) is considered. We discovered that the prescribed novel model is material for exception estimating and beats the strategies in light of auto relapse (AR) Intense Erudition Machines (IEMs) models.

Exploring the application of machine learning and python model lstm in predicting Tesla stock price

“…Application of the method in prediction. Starting from the causal relationship between market phenomena, through the establishment of regression prediction models [1]. In economic forecasting, people usually regard the prediction object as a dependent variable and the factors related to the prediction object as independent variables.…”