摘要翻译：
随着大数据的出现，在当今的许多应用中，包含大量相似时间序列的数据库是可用的。用传统的单变量预测方法对这些领域的时间序列进行预测，为产生准确的预测留下了巨大的潜力。最近，递归神经网络，特别是长短时记忆(LSTM)网络已经证明，当在所有可用的时间序列中训练时，它们能够在这种情况下优于现有的单变量时间序列预测方法。然而，如果时间序列数据库是异构的，精度可能会下降，因此在该空间向全自动预测方法发展的过程中，需要在方法中加入时间序列之间的相似性概念。为此，我们提出了一个预测模型，可以与不同类型的RNN模型一起使用，对相似的时间序列的子组进行预测，这些子组是通过时间序列聚类技术识别出来的。我们使用LSTM网络（一种广泛流行的RNN变体）来评估我们提出的方法。我们的方法在竞争评估程序下在基准数据集上获得竞争结果。特别是，在平均sMAPE精度方面，它始终优于基线LSTM模型，并在CIF2016预测竞争数据集上优于所有其他方法。
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英文标题：
《Forecasting Across Time Series Databases using Recurrent Neural Networks
  on Groups of Similar Series: A Clustering Approach》
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作者：
Kasun Bandara, Christoph Bergmeir, Slawek Smyl
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最新提交年份：
2018
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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：Computer Science        计算机科学
二级分类：Databases        数据库
分类描述：Covers database management, datamining, and data processing. Roughly includes material in ACM Subject Classes E.2, E.5, H.0, H.2, and J.1.
涵盖数据库管理、数据挖掘和数据处理。大致包括ACM学科类E.2、E.5、H.0、H.2和J.1中的材料。
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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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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英文摘要：
  With the advent of Big Data, nowadays in many applications databases containing large quantities of similar time series are available. Forecasting time series in these domains with traditional univariate forecasting procedures leaves great potentials for producing accurate forecasts untapped. Recurrent neural networks (RNNs), and in particular Long Short-Term Memory (LSTM) networks, have proven recently that they are able to outperform state-of-the-art univariate time series forecasting methods in this context when trained across all available time series. However, if the time series database is heterogeneous, accuracy may degenerate, so that on the way towards fully automatic forecasting methods in this space, a notion of similarity between the time series needs to be built into the methods. To this end, we present a prediction model that can be used with different types of RNN models on subgroups of similar time series, which are identified by time series clustering techniques. We assess our proposed methodology using LSTM networks, a widely popular RNN variant. Our method achieves competitive results on benchmarking datasets under competition evaluation procedures. In particular, in terms of mean sMAPE accuracy, it consistently outperforms the baseline LSTM model and outperforms all other methods on the CIF2016 forecasting competition dataset.
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PDF链接：
https://arxiv.org/pdf/1710.03222