Auto-Regressive Integrated Moving Average Threshold Influence Techniques for Stock Data Analysis

Abstract

This study focuses on predicting and estimating possible stock assets in a favorable real-time scenario for financial markets without the involvement of outside brokers about broadcast-based trading using various performance factors and data metrics. Sample data from the Y-finance sector was assembled using API-based data series and was quite accurate and precise. Prestigious machine learning algorithmic performances for both classification and regression complexities intensify this assumption. The fallibility of stock movement leads to the production of noise and vulnerability that relate to decision-making. In earlier research investigations, fewer performance metrics were used. In this study, Dickey-Fuller testing scenarios were combined with time series volatility forecasting and the Long Short-Term Memory algorithm, which was used in a futuristic recurrent neural network setting to predict future closing prices for large businesses on the stock market. In order to analyze the root mean squared error, mean squared error, mean absolute percentage error, mean deviation, and mean absolute error, this study combined LSTM methods with ARIMA. With fewer hardware resources, the experimental scenarios were framed, and test case simulations carried out. Keywords-Dickey-Fuller test case (DF-TC); recurrent neural network (RNN); root mean square error (RMSE); long short-term memory (LSTM); machine learning (ML); auto-regressive integrated moving average (ARIMA)I.

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

“…A DL model can effectively outperform traditional SPF systems in terms of accuracy. CNNs 50–54 and recurrent neural network (RNN) such as LSTM or gated recurrent unit (GRU), 55–60 and BiLSTM 61–64 are extensively employed for SPF systems. Table 3 presents a summary of DL-based approaches for SPF.…”