论文标题
多个周期滑动窗口汇总的快速自动功能选择时间序列
Fast Automatic Feature Selection for Multi-Period Sliding Window Aggregate in Time Series
论文作者
论文摘要
作为最著名的人造功能采样器之一，滑动窗口被广泛用于存在空间和时间信息的场景中，例如计算机视觉，自然语言过程，数据流和时间序列。在其中，在许多情况下，时间序列很常见，例如信用卡付款，用户行为和传感器。通过滑动窗口汇总调用提取的功能的一般功能选择，用于耗时迭代以生成功能，然后采用传统的功能选择方法来对其进行排名。关键参数的决定，即滑动窗口的周期，取决于域知识和呼吁微不足道。当前，没有自动方法来处理滑动窗口聚合特征选择。由于具有不同时期和滑动窗口的特征产生的时间消耗非常大，因此很难枚举它们，然后选择它们。 在本文中，我们建议使用马尔可夫链解决此问题的一般框架。该框架非常有效，精度具有很高的精度，因此它能够在各种功能和期间选项上执行功能选择。我们通过2个常见的滑动窗口和3种类型的聚合操作员来显示细节。通过采用有关马尔可夫链的现有理论，可以在此框架中扩展更多的滑动窗口和聚合操作员。
As one of the most well-known artificial feature sampler, the sliding window is widely used in scenarios where spatial and temporal information exists, such as computer vision, natural language process, data stream, and time series. Among which time series is common in many scenarios like credit card payment, user behavior, and sensors. General feature selection for features extracted by sliding window aggregate calls for time-consuming iteration to generate features, and then traditional feature selection methods are employed to rank them. The decision of key parameter, i.e. the period of sliding windows, depends on the domain knowledge and calls for trivial. Currently, there is no automatic method to handle the sliding window aggregate features selection. As the time consumption of feature generation with different periods and sliding windows is huge, it is very hard to enumerate them all and then select them. In this paper, we propose a general framework using Markov Chain to solve this problem. This framework is very efficient and has high accuracy, such that it is able to perform feature selection on a variety of features and period options. We show the detail by 2 common sliding windows and 3 types of aggregation operators. And it is easy to extend more sliding windows and aggregation operators in this framework by employing existing theory about Markov Chain.
