论文标题
预测时间序列的原理和算法：局部和全球性
Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality
论文作者
论文摘要
时间序列的预测组越来越重要，例如预测数据中心内零售商或服务器负载提供的多种产品的需求。该问题的本地方法分别考虑每个时间序列，并适合每个系列的函数或模型。全局方法适合所有系列功能。对于类似时间序列的组，全局方法的表现优于更确定的本地方法。但是，最近的结果表明，即使在异质数据集中，全球模型的性能都很好。这表明全球方法的更一般适用性，有可能导致更准确的工具和新的方案进行研究。 正式通过本地和全球方法进行预测设置一组时间序列，我们提供以下贡献： 1）全局方法比局部方法更具限制性，两者都可以产生相同的预测，而无需任何关于该系列的相似性的假设。全球模型可以在更广泛的问题中取得成功。 2）本地和全球算法的基本概括范围。局部方法的复杂性随着集合的大小而增长，而整体方法仍然是恒定的。在大型数据集中，全球算法可以负担得起非常复杂，并且仍然可以从更好的概括中受益。这些界限有助于澄清和支持该领域的最新实验结果，并指导新算法的设计。对于一类自回旋模型，这意味着与本地方法相比，全局模型的内存可能更大。 3）在一项广泛的实证研究中，故意幼稚的算法从这些原理中得出，例如全球线性模型或深网络，可以提高准确性。 特别是，与本地方法相比，全局线性模型可以提供竞争精度，而参数少两个。
Forecasting groups of time series is of increasing practical importance, e.g. forecasting the demand for multiple products offered by a retailer or server loads within a data center. The local approach to this problem considers each time series separately and fits a function or model to each series. The global approach fits a single function to all series. For groups of similar time series, global methods outperform the more established local methods. However, recent results show good performance of global models even in heterogeneous datasets. This suggests a more general applicability of global methods, potentially leading to more accurate tools and new scenarios to study. Formalizing the setting of forecasting a set of time series with local and global methods, we provide the following contributions: 1) Global methods are not more restrictive than local methods, both can produce the same forecasts without any assumptions about similarity of the series. Global models can succeed in a wider range of problems than previously thought. 2) Basic generalization bounds for local and global algorithms. The complexity of local methods grows with the size of the set while it remains constant for global methods. In large datasets, a global algorithm can afford to be quite complex and still benefit from better generalization. These bounds serve to clarify and support recent experimental results in the field, and guide the design of new algorithms. For the class of autoregressive models, this implies that global models can have much larger memory than local methods. 3) In an extensive empirical study, purposely naive algorithms derived from these principles, such as global linear models or deep networks result in superior accuracy. In particular, global linear models can provide competitive accuracy with two orders of magnitude fewer parameters than local methods.
