In Smyl et al. [Local and global trend Bayesian exponential smoothing models.
International Journal of Forecasting, 2024.], a generalised exponential
smoothing model was proposed that is able to capture strong trends and
volatility in time series. This method achieved state-of-the-art performance in
many forecasting tasks, but its fitting procedure, which is based on the NUTS
sampler, is very computationally expensive. In this work, we propose several
modifications to the original model, as well as a bespoke Gibbs sampler for
posterior exploration; these changes improve sampling time by an order of
magnitude, thus rendering the model much more practically relevant. The new
model, and sampler, are evaluated on the M3 dataset and are shown to be
competitive, or superior, in terms of accuracy to the original method, while
being substantially faster to run.

在 Smyl 等人提出的《局部和全局趋势贝叶斯指数平滑模型》研究中，提出了一种能够捕捉时间序列中强趋势和波动性的广义指数平滑模型。通过对原模型进行改进以及引入自定义的 Gibbs 采样器进行后验探索，该方法在时间序列预测任务中取得了最先进的性能，并且由于提高了采样速度一个数量级，使得该模型在实际应用中更具实用性。新模型和采样器在 M3 数据集上进行了评估，显示出在准确性上与原方法相比具有竞争力或更优越性能，并且运行速度更快。

局部和全局趋势的贝叶斯指数平滑模型的快速吉布斯抽样

Fast Gibbs sampling for the local and global trend Bayesian exponential  smoothing model

We introduce a new model for multivariate probabilistic time series
prediction, designed to flexibly address a range of tasks including
forecasting, interpolation, and their combinations. Building on copula theory,
we propose a simplified objective for the recently-introduced transformer-based
attentional copulas (TACTiS), wherein the number of distributional parameters
now scales linearly with the number of variables instead of factorially. The
new objective requires the introduction of a training curriculum, which goes
hand-in-hand with necessary changes to the original architecture. We show that
the resulting model has significantly better training dynamics and achieves
state-of-the-art performance across diverse real-world forecasting tasks, while
maintaining the flexibility of prior work, such as seamless handling of
unaligned and unevenly-sampled time series.

多元概率时间序列预测的新模型引入了 copula 理论，提出了基于 transformer 的简化目标函数 TACTiS，其分布参数数量与变量数量成线性关系。新目标函数引入了训练计划，并对原始架构进行必要的改变。结果表明，该模型在各种真实世界预测任务中具有显著改进的训练动力学，并实现了最先进的性能，同时保持了如处理不对齐和采样不均匀的时间序列等先前工作的灵活性。

TACTiS-2: 用于多元时间序列的更好、更快、更简单的注意力联合分布

TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate  Time Series

Envision an intelligent agent capable of assisting users in conducting
forecasting tasks through intuitive, natural conversations, without requiring
in-depth knowledge of the underlying machine learning (ML) processes. A
significant challenge for the agent in this endeavor is to accurately
comprehend the user's prediction goals and, consequently, formulate precise ML
tasks. In this paper, we take a pioneering step towards this ambitious goal by
introducing a new concept called Forecast Utterance and then focus on the
automatic and accurate interpretation of users' prediction goals from these
utterances. Specifically, we frame the task as a slot-filling problem, where
each slot corresponds to a specific aspect of the goal prediction task. We then
employ two zero-shot methods for solving the slot-filling task, namely: 1)
Entity Extraction (EE), and 2) Question-Answering (QA) techniques. Our
experiments, conducted with three meticulously crafted data sets, validate the
viability of our ambitious goal and demonstrate the effectiveness of both EE
and QA techniques in interpreting Forecast Utterances.

通过自然对话，智能代理能够协助用户进行预测任务，而无需深入了解机器学习过程，本文通过引入一种名为预测话语的新概念并聚焦于从这些话语中准确解读用户预测目标，将任务作为一个槽填充问题，然后采用两种零样本方法解决槽填充任务，即实体抽取和问答技术，我们通过三个经过精心构建的数据集进行实验证明了我们雄心勃勃的目标的可行性，并展示了实体抽取和问答技术在解读预测话语中的有效性。