[论文解读] A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM

Sima Siami‐Namini, Neda Tavakoli+1 more|arXiv (Cornell University)|Nov 21, 2019

Stock Market Forecasting Methods参考文献 22被引用 96

一句话总结

本论文将ARIMA、LSTM和BiLSTM用于金融时间序列预测进行比较，结果表明BiLSTM通常提供最佳预测准确性，尽管其训练速度比LSTM慢。

ABSTRACT

Machine and deep learning-based algorithms are the emerging approaches in addressing prediction problems in time series. These techniques have been shown to produce more accurate results than conventional regression-based modeling. It has been reported that artificial Recurrent Neural Networks (RNN) with memory, such as Long Short-Term Memory (LSTM), are superior compared to Autoregressive Integrated Moving Average (ARIMA) with a large margin. The LSTM-based models incorporate additional "gates" for the purpose of memorizing longer sequences of input data. The major question is that whether the gates incorporated in the LSTM architecture already offers a good prediction and whether additional training of data would be necessary to further improve the prediction. Bidirectional LSTMs (BiLSTMs) enable additional training by traversing the input data twice (i.e., 1) left-to-right, and 2) right-to-left). The research question of interest is then whether BiLSTM, with additional training capability, outperforms regular unidirectional LSTM. This paper reports a behavioral analysis and comparison of BiLSTM and LSTM models. The objective is to explore to what extend additional layers of training of data would be beneficial to tune the involved parameters. The results show that additional training of data and thus BiLSTM-based modeling offers better predictions than regular LSTM-based models. More specifically, it was observed that BiLSTM models provide better predictions compared to ARIMA and LSTM models. It was also observed that BiLSTM models reach the equilibrium much slower than LSTM-based models.

研究动机与目标

• 动机：评估现代学习方法相对于传统的ARIMA在金融预测中的有效性。
• 研究双向训练（BiLSTM）是否比单向LSTM提高预测准确性。
• 分析LSTM与BiLSTM在金融数据上的学习行为和训练动态。

提出的方法

• 使用ARIMA、LSTM和BiLSTM对来自多源的金融时间序列进行预测（日经225、IXIC、恒生指数、S&P 500、道琼斯工业平均、IBM）。
• 对所有模型仅使用调整收盘价作为特征。
• 应用滚动（再训练）预测，使用70/30的训练/测试拆分并测量RMSE。
• 实现一个单一代码库，在同一流程中切换LSTM与BiLSTM。
• 以RMSE及相对于ARIMA/LSTM基线的降幅百分比来评估性能。

实验结果

研究问题

• RQ1 双向学习是否相较于单向训练能提升预测？
• RQ2 LSTM与BiLSTM在输入数据处理和训练动态方面有何差异？
• RQ3 在金融时间序列的训练中，哪种架构更快达到收敛？

主要发现

• 在所测试的数据集上，BiLSTM通常在RMSE上优于LSTM和ARIMA，平均相对于LSTM的RMSE降低37.78%，相对于ARIMA的降低更多。
• BiLSTM相较于ARIMA的RMSE平均降低93.11%，相较于LSTM的平均降低37.78%。
• BiLSTM需要使用更多的数据批次进行训练，且收敛速度通常慢于LSTM（初期损失较高但随后持续改善）。
• 对于所考察的数据集，BiLSTM的平均RMSE为20.17（LSTM为39.09）。
• 由于双向训练，BiLSTM倾向使用更小的批次大小（71–75），而LSTM为41–42。