Predicting Financial Enterprise Stocks and Economic Data Trends Using Machine Learning Time Series Analysis

Опубликована: Июль 11, 2024

This paper explores the application of machine learning in financial time series analysis, focusing on predicting trends enterprise stocks and economic data. It begins by distinguishing from elucidates risk management strategies stock market. Traditional statistical methods such as ARIMA exponential smoothing are discussed terms their advantages limitations forecasting. Subsequently, effectiveness techniques, particularly LSTM CNN-BiLSTM hybrid models, market prediction is detailed, highlighting capability to capture nonlinear patterns dynamic markets. The study demonstrates advancements predictive accuracy robustness achieved deep through empirical analysis model validation. findings contribute significantly academic discourse offer practical insights for investors, analysts, policymakers navigating volatility optimizing investment strategies. Finally, outlines prospects forecasting, laying a theoretical foundation methodological framework achieving more precise reliable predictions.

Язык: Английский

The Contribution of Federated Learning to AI Development

Опубликована: Июль 5, 2024

With the widespread application of artificial intelligence technology in various industries, users' attention to privacy and data security has increased significantly. Federated learning, as a new paradigm combining privacy-enhanced computing intelligence, resolves contradiction between open sharing. This paper presents benefits federated learning terms privacy, real-time processing, model robustness, compliance cross-industry applications. At same time, when combined with Edge AI technology, promotes decentralisation intelligent systems, improving protection accuracy. also discusses cases medical field, through local processing training, effectively protecting user realizing sharing optimization, promoting development intelligence.

Язык: Английский

Enhancing Media Convergence with Artificial Intelligence to Stabilize Financial Markets

Опубликована: Янв. 14, 2025

This study explores the application of artificial intelligence (AI) technology in media convergence, focusing on how AI is driving deep integration and financial markets through big data analytics, AIGC (AI-generated content), intelligent communication technologies. Ai-driven sentiment analysis fake news detection tools effectively solve problem information asymmetry spread false market promote stability transparency. Through personalized recommendations communication, provides users with a more accurate content experience improves user engagement satisfaction. In addition, rapid development ecology has promoted intellectualization dissemination public opinion analysis, providing forward-looking support for decision-making.

Язык: Английский