英文标题：
《Forecasting Economics and Financial Time Series: ARIMA vs. LSTM》
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作者：
Sima Siami-Namini and Akbar Siami Namin
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最新提交年份：
2018
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英文摘要：
  Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques to effectively forecast the next lag of time series data such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Autoregressive Integrated Moving Average (ARIMA) with its many variations. In particular, ARIMA model has demonstrated its outperformance in precision and accuracy of predicting the next lags of time series. With the recent advancement in computational power of computers and more importantly developing more advanced machine learning algorithms and approaches such as deep learning, new algorithms are developed to forecast time series data. The research question investigated in this article is that whether and how the newly developed deep learning-based algorithms for forecasting time series data, such as \"Long Short-Term Memory (LSTM)\", are superior to the traditional algorithms. The empirical studies conducted and reported in this article show that deep learning-based algorithms such as LSTM outperform traditional-based algorithms such as ARIMA model. More specifically, the average reduction in error rates obtained by LSTM is between 84 - 87 percent when compared to ARIMA indicating the superiority of LSTM to ARIMA. Furthermore, it was noticed that the number of training times, known as \"epoch\" in deep learning, has no effect on the performance of the trained forecast model and it exhibits a truly random behavior.
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中文摘要：
预测时间序列数据是经济学、商业和金融学中的一个重要课题。传统上，有几种技术可以有效预测时间序列数据的下一个滞后，如单变量自回归（AR）、单变量移动平均（MA）、简单指数平滑（SES），以及更显著的具有多种变化的自回归综合移动平均（ARIMA）。特别是，ARIMA模型在预测未来时间序列滞后的精度和准确性方面表现出了卓越的性能。随着计算机计算能力的不断提高，更重要的是，随着更先进的机器学习算法和方法（如深度学习）的发展，人们开发了新的算法来预测时间序列数据。本文研究的问题是，新开发的基于深度学习的时间序列数据预测算法，如“长-短期记忆（LSTM）”是否以及如何优于传统算法。本文进行和报告的实证研究表明，基于深度学习的算法（如LSTM）优于基于ARIMA模型的传统算法。更具体地说，与ARIMA相比，LSTM获得的错误率平均减少84-87%，表明LSTM优于ARIMA。此外，我们还注意到，深度学习中称为“epoch”的训练次数对训练后的预测模型的性能没有影响，它表现出真正的随机行为。
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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