LSTM比传统模型更准预测尼日利亚股市，但需更多算力且难解释。
Stock Price Prediction and Traditional Models: An Approach to Achieve Short-, Medium- and Long-Term Goals
- 用LSTM和GRU等深度学习模型捕捉股价非线性变化规律。
- 在1年、2.5年、5年三个周期上，LSTM的误差均低于传统方法。
- 适合金融建模者参考，尤其关注长期预测精度提升的人群。
本文对比了深度学习模型与传统统计方法在尼日利亚证券交易所股价预测中的表现。基于每日价格和交易量的历史数据，采用LSTM、GRU、ARIMA和ARMA等模型，在短期（1年）、中期（2.5年）和长期（5年）三个时间维度上进行评估，使用均方误差（MSE）和平均绝对误差（MAE）衡量性能。通过增强迪基-富勒（ADF）检验验证时间序列稳定性。结果表明，深度学习模型特别是LSTM能更好捕捉数据中的复杂非线性模式，预测更准确；但其计算成本更高，可解释性差于传统方法。研究揭示了深度学习在金融预测中的潜力，未来可融合社交媒体情绪、经济指标等外部因素，优化模型结构并探索实时应用以提升准确性与扩展性。
原文摘要 · Abstract (English)
A comparative analysis of deep learning models and traditional statistical methods for stock price prediction uses data from the Nigerian stock exchange. Historical data, including daily prices and trading volumes, are employed to implement models such as Long Short Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Autoregressive Integrated Moving Average (ARIMA), and Autoregressive Moving Average (ARMA). These models are assessed over three-time horizons: short-term (1 year), medium-term (2.5 years), and long-term (5 years), with performance measured by Mean Squared Error (MSE) and Mean Absolute Error (MAE). The stability of the time series is tested using the Augmented Dickey-Fuller (ADF) test. Results reveal that deep learning models, particularly LSTM, outperform traditional methods by capturing complex, nonlinear patterns in the data, resulting in more accurate predictions. However, these models require greater computational resources and offer less interpretability than traditional approaches. The findings highlight the potential of deep learning for improving financial forecasting and investment strategies. Future research could incorporate external factors such as social media sentiment and economic indicators, refine model architectures, and explore real-time applications to enhance prediction accuracy and scalability.
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