Time series methods are of fundamental importance in virtually any field of
science that deals with temporally structured data. Recently, there has been a
surge of deterministic transformer models with time series-specific
architectural biases. In this paper, we go in a different direction by
introducing $\mathtt{tsGT}$, a stochastic time series model built on a
general-purpose transformer architecture. We focus on using a well-known and
theoretically justified rolling window backtesting and evaluation protocol. We
show that $\mathtt{tsGT}$ outperforms the state-of-the-art models on MAD and
RMSE, and surpasses its stochastic peers on QL and CRPS, on four commonly used
datasets. We complement these results with a detailed analysis of
$\mathtt{tsGT}$'s ability to model the data distribution and predict marginal
quantile values.

本论文介绍了一种基于通用 Transformer 架构的随机时间序列模型 tsGT，并使用滚动窗口回测和评估协议展示了其在四个常用数据集上在 MAD 和 RMSE 上优于现有模型，在 QL 和 CRPS 上超越了其随机同行模型，并补充了对 tsGT 建模数据分布和预测边际分位值能力的详细分析。

基于 Transformer 的随机时间序列建模

$\mathtt{tsGT}$: Stochastic Time Series Modeling With Transformer

As one of the most extensive social networking services, Twitter has more
than 300 million active users as of 2022. Among its many functions, Twitter is
now one of the go-to platforms for consumers to share their opinions about
products or experiences, including flight services provided by commercial
airlines. This study aims to measure customer satisfaction by analyzing
sentiments of Tweets that mention airlines using a machine learning approach.
Relevant Tweets are retrieved from Twitter's API and processed through
tokenization and vectorization. After that, these processed vectors are passed
into a pre-trained machine learning classifier to predict the sentiments. In
addition to sentiment analysis, we also perform lexical analysis on the
collected Tweets to model keywords' frequencies, which provide meaningful
contexts to facilitate the interpretation of sentiments. We then apply time
series methods such as Bollinger Bands to detect abnormalities in sentiment
data. Using historical records from January to July 2022, our approach is
proven to be capable of capturing sudden and significant changes in passengers'
sentiment. This study has the potential to be developed into an application
that can help airlines, along with several other customer-facing businesses,
efficiently detect abrupt changes in customers' sentiments and take adequate
measures to counteract them.

本研究旨在使用机器学习方法分析 Twitter 上提到的航空公司航班服务的客户满意度，通过情感分析、词汇分析和时间序列分析，检测顾客情感的异常波动，以帮助航空公司及其他客户面对顾客的快速变化而采取适当措施。