Recent studies have shown the ability of large language models to perform a
variety of tasks, including time series forecasting. The flexible nature of
these models allows them to be used for many applications. In this paper, we
present a novel study of large language models used for the challenging task of
time series anomaly detection. This problem entails two aspects novel for LLMs:
the need for the model to identify part of the input sequence (or multiple
parts) as anomalous; and the need for it to work with time series data rather
than the traditional text input. We introduce sigllm, a framework for time
series anomaly detection using large language models. Our framework includes a
time-series-to-text conversion module, as well as end-to-end pipelines that
prompt language models to perform time series anomaly detection. We investigate
two paradigms for testing the abilities of large language models to perform the
detection task. First, we present a prompt-based detection method that directly
asks a language model to indicate which elements of the input are anomalies.
Second, we leverage the forecasting capability of a large language model to
guide the anomaly detection process. We evaluated our framework on 11 datasets
spanning various sources and 10 pipelines. We show that the forecasting method
significantly outperformed the prompting method in all 11 datasets with respect
to the F1 score. Moreover, while large language models are capable of finding
anomalies, state-of-the-art deep learning models are still superior in
performance, achieving results 30% better than large language models.

本研究通过 sigllm 框架探索了大型语言模型在时间序列异常检测方面的应用，包括时间序列转文本处理模块和基于预测的探测方法，并在 11 个数据集上进行了评估，结果显示预测方法在 F1 分数上明显优于提示方法，但与最先进的深度学习模型相比，大型语言模型的性能仍略逊，差距约为 30%。

大型语言模型可以用作时间序列的零样本异常检测器吗？

Large language models can be zero-shot anomaly detectors for time  series?

Designing a humanoid locomotion controller is challenging and classically
split up in sub-problems. Footstep planning is one of those, where the sequence
of footsteps is defined. Even in simpler environments, finding a minimal
sequence, or even a feasible sequence, yields a complex optimization problem.
In the literature, this problem is usually addressed by search-based algorithms
(e.g. variants of A*). However, such approaches are either computationally
expensive or rely on hand-crafted tuning of several parameters. In this work,
at first, we propose an efficient footstep planning method to navigate in local
environments with obstacles, based on state-of-the art Deep Reinforcement
Learning (DRL) techniques, with very low computational requirements for on-line
inference. Our approach is heuristic-free and relies on a continuous set of
actions to generate feasible footsteps. In contrast, other methods necessitate
the selection of a relevant discrete set of actions. Second, we propose a
forecasting method, allowing to quickly estimate the number of footsteps
required to reach different candidates of local targets. This approach relies
on inherent computations made by the actor-critic DRL architecture. We
demonstrate the validity of our approach with simulation results, and by a
deployment on a kid-size humanoid robot during the RoboCup 2023 competition.

设计人形机器人的运动控制器是具有挑战性的，其中步态规划是其中一个子问题；我们提出了一种基于深度强化学习技术的高效步态规划方法，来在具有障碍物的局部环境中进行导航，并且提出了一种预测方法来快速估计到达不同的局部目标所需的步数，通过仿真结果和在 RoboCup 2023 比赛中的实际部署验证了我们的方法。

FootstepNet：一种高效的演员 - 评论家方法，用于快速的在线双足步行脚步规划和预测

FootstepNet: an Efficient Actor-Critic Method for Fast On-line Bipedal  Footstep Planning and Forecasting

Accurate and timely prediction of sea fog is very important for effectively
managing maritime and coastal economic activities. Given the intricate nature
and inherent variability of sea fog, traditional numerical and statistical
forecasting methods are often proven inadequate. This study aims to develop an
advanced sea fog forecasting method embedded in a numerical weather prediction
model using the Yangtze River Estuary (YRE) coastal area as a case study. Prior
to training our machine learning model, we employ a time-lagged correlation
analysis technique to identify key predictors and decipher the underlying
mechanisms driving sea fog occurrence. In addition, we implement ensemble
learning and a focal loss function to address the issue of imbalanced data,
thereby enhancing the predictive ability of our model. To verify the accuracy
of our method, we evaluate its performance using a comprehensive dataset
spanning one year, which encompasses both weather station observations and
historical forecasts. Remarkably, our machine learning-based approach surpasses
the predictive performance of two conventional methods, the weather research
and forecasting nonhydrostatic mesoscale model (WRF-NMM) and the algorithm
developed by the National Oceanic and Atmospheric Administration (NOAA)
Forecast Systems Laboratory (FSL). Specifically, in regard to predicting sea
fog with a visibility of less than or equal to 1 km with a lead time of 60
hours, our methodology achieves superior results by increasing the probability
of detection (POD) while simultaneously reducing the false alarm ratio (FAR).

基于机器学习的方法，通过数值天气预报模型，对扬子江河口（YRE）沿海地区进行了先进的海雾预测方法开发，验证了其在海雾的准确性方面的优势。