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Deep Adaptive Input Normalization for Time Series Forecasting
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DOI：10.1109/TNNLS.2019.2944933.png)
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摘要
En 中文
Deep learning (DL) models can be used to tackle time series analysis tasks with great success. However, the performance of DL models can degenerate rapidly if the data are not appropriately normalized. This issue is even more apparent when DL is used for financial time series forecasting tasks, where the nonstationary and multimodal nature of the data pose significant challenges and severely affect the performance of DL models. In this brief, a simple, yet effective, neural layer that is capable of adaptively normalizing the input time series, while taking into account the distribution of the data, is proposed. The proposed layer is trained in an end-to-end fashion using backpropagation and leads to significant performance improvements compared to other evaluated normalization schemes. The proposed method differs from traditional normalization methods since it learns how to perform normalization for a given task instead of using a fixed normalization scheme. At the same time, it can be directly applied to any new time series without requiring retraining. The effectiveness of the proposed method is demonstrated using a large-scale limit order book data set, as well as a load forecasting data set.
Keyword:
Time series analysis
Task analysis
Data models
Forecasting
Training
Predictive models
Adaptation models
Data normalization
deep learning (DL)
limit order book data
time series forecasting
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Time series forecasting using a deep belief network with restricted Boltzmann machines使用具有受限玻尔兹曼机的深度信念网络进行时间序列预测.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Benchmark dataset for mid-price forecasting of limit order book data with machine learning methodsOAAI
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