Multivariate Time Series Forecasting Based Cloud Computing For Consumer Price Index Using Deep Learning Algorithms

A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

“…Despite the increasing development and application of hybrid data analysis techniques based on neural network learning approaches for stock market analysis, current models incorporating quantitative stock data and news data largely consider the extraction of information sentiment polarities as a support rather than an integral part of stock trend prediction. Most previous studies have used Twitter and Twitter texts as a source of information data to better convey sentiment [2,5,10,14,20,21,47]. However, considering that news reflects perceived reality and sentiment polarity is usually fuzzy, improving predictive accuracy by highlighting opinions cannot be taken for granted.…”

“…After the above calculation, the implicit state code h N of the headline n at time t = N is obtained. Based on h N , the vector of the probability distribution of j in the different sentiment categories [13,14], i.e., positive and negative, is obtained by the Soft-Max function as shown in Equation (8). Based on y j , the sentiment orientation (SO) of heading n is then calculated as shown in Equation (9).…”

“…Second, the current study relies on a single stock-related information source, which may limit the predictive power of the proposed model. Indeed, stock markets are typically influenced by a variety of textbased information sources, such as monetary news, online media, websites, or corporate statements [8,14,31,38,51,52]. These information sources differ in the way they influence monetary economic entities.…”

“…Although stock price movements are stochastic and generally involve non-random events, they can still be predicted by analyzing investor behavior and trading patterns [12][13][14]. While econometrics-based statistical methods can rely on tentative premises, machine learning methods pose challenges due to limited interpretability, the need for manual feature selection, and the problem of overfitting.…”