In order to support the advancement of machine learning methods for
predicting time-series data, we present a comprehensive dataset designed
explicitly for long-term time-series forecasting. We incorporate a collection
of datasets obtained from diverse, dynamic systems and real-life records. Each
dataset is standardized by dividing it into training and test trajectories with
predetermined lookback lengths. We include trajectories of length up to $2000$
to ensure a reliable evaluation of long-term forecasting capabilities. To
determine the most effective model in diverse scenarios, we conduct an
extensive benchmarking analysis using classical and state-of-the-art models,
namely LSTM, DeepAR, NLinear, N-Hits, PatchTST, and LatentODE. Our findings
reveal intriguing performance comparisons among these models, highlighting the
dataset-dependent nature of model effectiveness. Notably, we introduce a custom
latent NLinear model and enhance DeepAR with a curriculum learning phase. Both
consistently outperform their vanilla counterparts.

为了支持机器学习方法在预测时间序列数据方面的进展，我们提供了一个专门设计用于长期时间序列预测的全面数据集。我们将从不同的动态系统和真实记录中获取的一系列数据集进行了标准化，每个数据集都被分成了训练和测试轨迹，并设定了预定的回溯长度。通过对经典和先进的模型（即 LSTM，DeepAR，NLinear，N-Hits，PatchTST，和 LatentODE）进行广泛的基准分析，我们确定在不同场景中最有效的模型。我们的研究结果显示出这些模型之间有着有趣的性能比较，突显了模型效果与数据集有关的特性。值得注意的是，我们引入了一个定制的 latent NLinear 模型并且通过课程学习阶段提高了 DeepAR 的性能，它们在效果上始终优于其原始版本。

统一长期时间序列预测基准

Unified Long-Term Time-Series Forecasting Benchmark

Video description is one of the most challenging problems in vision and
language understanding due to the large variability both on the video and
language side. Models, hence, typically shortcut the difficulty in recognition
and generate plausible sentences that are based on priors but are not
necessarily grounded in the video. In this work, we explicitly link the
sentence to the evidence in the video by annotating each noun phrase in a
sentence with the corresponding bounding box in one of the frames of a video.
Our dataset, ActivityNet-Entities, augments the challenging ActivityNet
Captions dataset with 158k bounding box annotations, each grounding a noun
phrase. This allows training video description models with this data, and
importantly, evaluate how grounded or "true" such model are to the video they
describe. To generate grounded captions, we propose a novel video description
model which is able to exploit these bounding box annotations. We demonstrate
the effectiveness of our model on our dataset, but also show how it can be
applied to image description on the Flickr30k Entities dataset. We achieve
state-of-the-art performance on video description, video paragraph description,
and image description and demonstrate our generated sentences are better
grounded in the video.

该文介绍了利用视频中物体的 bounding box 将描述影片的句子与影片中的客观证据联系起来，并给出了一个能够利用 bounding box 的词语注释的视频描述模型，其在视频描述、视频段落描述和图像描述上均表现出了最先进的性能和更好的联系性。