时间序列预测：股票价格预测
Time Series Prediction : Predicting Stock Price
摘要：
时间序列预测广泛应用于众多领域。在本文中，我们提出了四个模型来预测股票价格使用SPX指数作为输入时间序列数据。鞅模型和普通线性模型需要最强的平稳性假设，我们将其用作基线模型。广义线性模型需要较少的假设，但无法优于鞅。在实证测试中，RNN模型与其他两个模型相比表现最好，因为它会通过LSTM即时更新输入，但也不会击败鞅。此外，我们还介绍了一种在线批量算法和差异度量，以告知读者时间序列预测方法的最新研究，它不需要任何平稳性或非混合假设的时间序列数据。最后，为了将这些预测应用于实践，我们介绍了可以创造双赢和零和局面的基本交易策略。
英文摘要：
Time series forecasting is widely used in a multitude of domains. In this paper, we present four models to predict the stock price using the SPX index as input time series data. The martingale and ordinary linear models require the strongest assumption in stationarity which we use as baseline models. The generalized linear model requires lesser assumptions but is unable to outperform the martingale. In empirical testing, the RNN model performs the best comparing to other two models, because it will update the input through LSTM instantaneously, but also does not beat the martingale. In addition, we introduce an online to batch algorithm and discrepancy measure to inform readers the newest research in time series predicting method, which doesn't require any stationarity or non mixing assumptions in time series data. Finally, to apply these forecasting to practice, we introduce basic trading strategies that can create Win win and Zero sum situations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
