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Forecasting Financial Time Series Using Robust Deep Adaptive Input Normalization
Journal of Signal Processing Systems ( IF 1.8 ) Pub Date : 2021-01-22 , DOI: 10.1007/s11265-020-01624-0
Nikolaos Passalis , Juho Kanniainen , Moncef Gabbouj , Alexandros Iosifidis , Anastasios Tefas

Deep Learning provided powerful tools for forecasting financial time series data. However, despite the success of these approaches on many challenging financial forecasting tasks, it is not always straightforward to employ DL-based approaches for highly volatile and non-stationary time financial series. To this end, in this paper, an adaptive input normalization layer that can learn to identify the distribution from which the input data were generated and then apply the most appropriate normalization scheme is proposed. This allows for promptly adapting the input to the subsequent DL model, which can be especially important, given recent findings that hint at the existence of critical learning periods in neural networks. Furthermore, the proposed method operates on a sliding window over the time series allowing for overcoming non-stationary issues that often arise. It is worth noting that the main difference with existing approaches is that the proposed method does not just learn to perform static normalization, e.g., using a fixed set of parameters, but instead it adaptively calculates the most appropriate normalization parameters, significantly improving the robustness of the proposed approach when distribution shifts occur. The effectiveness of the proposed formulation is verified using extensive experiments on three challenging financial time-series datasets.



中文翻译:

使用稳健的深度自适应输入归一化预测财务时间序列

深度学习提供了用于预测财务时间序列数据的强大工具。但是,尽管这些方法在许多具有挑战性的财务预测任务中都取得了成功,但对于高波动性和非固定时间的财务系列而言,采用基于DL的方法并不总是那么容易。为此,本文提出了一种自适应输入归一化层,该层可以学习识别生成输入数据的分布,然后应用最合适的归一化方案。鉴于最近的发现暗示了神经网络中关键学习阶段的存在,这可以使输入迅速适应后续的DL模型,这一点尤其重要。此外,所提出的方法在时间序列上的滑动窗口上运行,可以克服经常出现的非平稳问题。值得注意的是,与现有方法的主要区别在于,所提出的方法不仅仅学会执行静态归一化(例如,使用一组固定的参数),而是自适应地计算最合适的归一化参数,从而大大提高了鲁棒性。分布转移时的建议方法。通过对三个具有挑战性的金融时间序列数据集进行广泛的实验,验证了所提出配方的有效性。而是自适应地计算最合适的归一化参数,从而在分布发生变化时显着提高了所提出方法的鲁棒性。通过对三个具有挑战性的金融时间序列数据集进行广泛的实验,验证了所提出配方的有效性。而是自适应地计算最合适的归一化参数,从而在分布发生变化时显着提高了所提出方法的鲁棒性。通过对三个具有挑战性的金融时间序列数据集进行广泛的实验,验证了所提出配方的有效性。

更新日期:2021-01-22
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