Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoML
通过数据增强与 AutoML 改进神经网络时间序列预测AI 解读 ↓
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通过数据增强与 AutoML 改进神经网络时间序列预测
- 研究问题
- 中等长度时间序列难以支撑大量神经网络参数时，如何利用统计模型预测进行数据增强，并通过自动化架构选择改善预测？
- 方法与思路
- 作者提出 Augmented-Neural-Network，利用统计模型的预测增强数据，再结合 AutoML 和神经架构搜索选择适合序列的网络。
- 材料陈述的贡献
- 摘要称该组合在一个 COVID-19 数据集上提高了三种神经网络模型的预测精度，并相对未增强数据的网络报告最大改善幅度。
- 阅读关注点
- 应核对增强样本如何生成、统计预测是否仅使用当时可得信息，以及架构搜索与测试集的隔离、精度指标及基准公平性。
- 阅读边界
- 所据为 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未定义精度指标和完整验证流程，单一数据集结果不能普遍化。
原文摘要
Statistical methods such as the Box-Jenkins method for time-series forecasting have been prominent since their development in 1970. Many researchers rely on such models as they can be efficiently estimated and also provide interpretability. However, advances in machine learning research indicate that neural networks can be powerful data modeling techniques, as they can give higher accuracy for a plethora of learning problems and datasets. In the past, they have been tried on time-series forecasting as well, but their overall results have not been significantly better than the statistical models especially for intermediate length times series data. Their modeling capacities are limited in cases where enough data may not be available to estimate the large number of parameters that these non-linear models require. This paper presents an easy to implement data augmentation method to significantly improve the performance of such networks. Our method, Augmented-Neural-Network, which involves using forecasts from statistical models, can help unlock the power of neural networks on intermediate length time-series and produces competitive results. It shows that data augmentation, when paired with Automated Machine Learning techniques such as Neural Architecture Search, can help to find the best neural architecture for a given time-series. Using the combination of these, demonstrates significant enhancement in the forecasting accuracy of three neural network-based models for a COVID-19 dataset, with a maximum improvement in forecasting accuracy by 21.41%, 24.29%, and 16.42%, respectively, over the neural networks that do not use augmented data.