Modern Economics & Management Forum

Objective: This paper explores the advanced applications of Python in market trend analysis, combining time series analysis, machine learning, and deep learning techniques to construct an efficient market trend forecasting framework, thereby improving the scientific and accurate decision-making process. Methods: Empirical analysis is conducted using historical financial market data to construct three models: ARIMA, LSTM, and SVR. Python is used for data preprocessing, model training, and prediction evaluation. The models' accuracy, stability, and robustness are comprehensively compared using three evaluation metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and Trend Consistency Rate (TCR). Results: The experiments demonstrate that the LSTM model performs best in terms of prediction accuracy, trend consistency, and robustness, with the lowest MSE (0.015), lowest MAE (0.088), and highest TCR (85.3%). The SVR model ranks second, while ARIMA performs weakly when dealing with nonlinear data. Conclusion: Python, with its powerful data analysis capabilities and ease of algorithm implementation, provides comprehensive support for market trend analysis. The LSTM model, due to its ability to model non-linear relationships and capture long-term dependencies, is suitable for complex trend forecasting and provides valuable insights for market decision-making.