Time series forecasting attempts to predict future events by analyzing past
trends and patterns. Although well researched, certain critical aspects
pertaining to the use of deep learning in time series forecasting remain
ambiguous. Our research primarily focuses on examining the impact of specific
hyperparameters related to time series, such as context length and validation
strategy, on the performance of the state-of-the-art MLP model in time series
forecasting. We have conducted a comprehensive series of experiments involving
4800 configurations per dataset across 20 time series forecasting datasets, and
our findings demonstrate the importance of tuning these parameters.
Furthermore, in this work, we introduce the largest metadataset for timeseries
forecasting to date, named TSBench, comprising 97200 evaluations, which is a
twentyfold increase compared to previous works in the field. Finally, we
demonstrate the utility of the created metadataset on multi-fidelity
hyperparameter optimization tasks.

我们的研究主要关注于考察与时间序列相关的特定超参数（如上下文长度和验证策略）对时间序列预测中最先进的 MLP 模型性能的影响，通过对 20 个时间序列预测数据集进行了 4800 种配置的广泛实验，我们的研究结果表明这些参数的调整对性能至关重要。此外，我们还引入了迄今为止最大的用于时间序列预测的元数据集 TSBench，包括 97200 个评估，相较于该领域的先前作品增加了 20 倍。最后，我们展示了该创建的元数据集在多保真度超参数优化任务中的实用性。

基于概率的时间序列预测中多层感知器神经网络的超参数调优

Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting

Popular approaches for few-shot classification consist of first learning a
generic data representation based on a large annotated dataset, before adapting
the representation to new classes given only a few labeled samples. In this
work, we propose a new strategy based on feature selection, which is both
simpler and more effective than previous feature adaptation approaches. First,
we obtain a multi-domain representation by training a set of semantically
different feature extractors. Then, given a few-shot learning task, we use our
multi-domain feature bank to automatically select the most relevant
representations. We show that a simple non-parametric classifier built on top
of such features produces high accuracy and generalizes to domains never seen
during training, which leads to state-of-the-art results on MetaDataset and
improved accuracy on mini-ImageNet.

本篇论文提出了一种基于特征选择的新策略，可以更加简单和有效地应对 few-shot 分类问题。该方法首先训练多个具有不同语义的特征提取器，然后根据特定任务的需求自动选择最相关的特征表示，并在此基础上构建简单的非参数分类器，可以在 MetaDataset 数据集上实现最新的结果及 mini-ImageNet 上实现进一步准确度的提高