We present an open-source Python library for building and using datasets
where inputs are clusters of textual data, and outputs are sequences of real
values representing one or more time series signals. The news-signals library
supports diverse data science and NLP problem settings related to the
prediction of time series behaviour using textual data feeds. For example, in
the news domain, inputs are document clusters corresponding to daily news
articles about a particular entity, and targets are explicitly associated
real-valued time series: the volume of news about a particular person or
company, or the number of pageviews of specific Wikimedia pages. Despite many
industry and research use cases for this class of problem settings, to the best
of our knowledge, News Signals is the only open-source library designed
specifically to facilitate data science and research settings with natural
language inputs and time series targets. In addition to the core codebase for
building and interacting with datasets, we also conduct a suite of experiments
using several popular Machine Learning libraries, which are used to establish
baselines for time series anomaly prediction using textual inputs.

这篇研究论文介绍了一个开源的 Python 库，用于构建和使用包含文本数据集的数据，其中输入是文本数据的聚类，输出是表示一个或多个时间序列信号的实值序列。该库支持与文本数据提供相关的时间序列行为预测的各种数据科学和自然语言处理问题设置，并与其他机器学习库一起进行了一系列实验，以建立基于文本输入的时间序列异常预测的基线。

新闻信号：一种用于文本和时间序列的 NLP 库

News Signals: An NLP Library for Text and Time Series

Long Short Term Memory Fully Convolutional Neural Networks (LSTM-FCN) and
Attention LSTM-FCN (ALSTM-FCN) have shown to achieve state-of-the-art
performance on the task of classifying time series signals on the old
University of California-Riverside (UCR) time series repository. However, there
has been no study on why LSTM-FCN and ALSTM-FCN perform well. In this paper, we
perform a series of ablation tests (3627 experiments) on LSTM-FCN and ALSTM-FCN
to provide a better understanding of the model and each of its sub-module.
Results from the ablation tests on ALSTM-FCN and LSTM-FCN show that the LSTM
and the FCN blocks perform better when applied in a conjoined manner. Two
z-normalizing techniques, z-normalizing each sample independently and
z-normalizing the whole dataset, are compared using a Wilcoxson signed-rank
test to show a statistical difference in performance. In addition, we provide
an understanding of the impact dimension shuffle has on LSTM-FCN by comparing
its performance with LSTM-FCN when no dimension shuffle is applied. Finally, we
demonstrate the performance of the LSTM-FCN when the LSTM block is replaced by
a GRU, basic RNN, and Dense Block.

本文通过 3627 个实验，研究了 LSTM-FCN 和 ALSTM-FCN 的模型和子模块，比较了两种 z-normalizing 技术的差异，以及提供了 LSTM-FCN 在 GRU、基础 RNN 和 Dense Block 下的性能表现。