Time-series representation learning is a key area of research for remote
healthcare monitoring applications. In this work, we focus on a dataset of
recordings of in-home activity from people living with Dementia. We design a
representation learning method based on converting activity to text strings
that can be encoded using a language model fine-tuned to transform data from
the same participants within a $30$-day window to similar embeddings in the
vector space. This allows for clustering and vector searching over participants
and days, and the identification of activity deviations to aid with
personalised delivery of care.

研究了时序表示学习在远程医疗监测应用中的关键领域。基于老年痴呆症患者在家活动的数据集，设计了一种表示学习方法，将活动转化为可用语言模型编码的文本字符串。通过利用矢量空间中相似嵌入，实现了参与者和天数的聚类和向量搜索，并用于识别活动异常以帮助进行个性化护理。

利用文本编码器对日常移动数据进行表示学习

Representation Learning of Daily Movement Data Using Text Encoders

Time-series representation learning can extract representations from data
with temporal dynamics and sparse labels. When labeled data are sparse but
unlabeled data are abundant, contrastive learning, i.e., a framework to learn a
latent space where similar samples are close to each other while dissimilar
ones are far from each other, has shown outstanding performance. This strategy
can encourage varied consistency of time-series representations depending on
the positive pair selection and contrastive loss. We propose a new time-series
representation learning method by combining the advantages of self-supervised
tasks related to contextual, temporal, and transformation consistency. It
allows the network to learn general representations for various downstream
tasks and domains. Specifically, we first adopt data preprocessing to generate
positive and negative pairs for each self-supervised task. The model then
performs contextual, temporal, and transformation contrastive learning and is
optimized jointly using their contrastive losses. We further investigate an
uncertainty weighting approach to enable effective multi-task learning by
considering the contribution of each consistency. We evaluate the proposed
framework on three downstream tasks: time-series classification, forecasting,
and anomaly detection. Experimental results show that our method not only
outperforms the benchmark models on these downstream tasks, but also shows
efficiency in cross-domain transfer learning.

本文提出了一种新的时间序列表示学习方法，通过自监督任务的上下文、时间和转换一致性，结合不确定性加权方法，实现了多任务学习和跨领域传递学习，进而在时间序列分类、预测和异常检测等下游任务中取得了更好表现。

多任务自监督时间序列表示学习

Multi-Task Self-Supervised Time-Series Representation Learning

Learning decent representations from unlabeled time-series data with temporal
dynamics is a very challenging task. In this paper, we propose an unsupervised
Time-Series representation learning framework via Temporal and Contextual
Contrasting (TS-TCC), to learn time-series representation from unlabeled data.
First, the raw time-series data are transformed into two different yet
correlated views by using weak and strong augmentations. Second, we propose a
novel temporal contrasting module to learn robust temporal representations by
designing a tough cross-view prediction task. Last, to further learn
discriminative representations, we propose a contextual contrasting module
built upon the contexts from the temporal contrasting module. It attempts to
maximize the similarity among different contexts of the same sample while
minimizing similarity among contexts of different samples. Experiments have
been carried out on three real-world time-series datasets. The results manifest
that training a linear classifier on top of the features learned by our
proposed TS-TCC performs comparably with the supervised training. Additionally,
our proposed TS-TCC shows high efficiency in few-labeled data and transfer
learning scenarios. The code is publicly available at
this https URL

本文提出了一种通过时间和语境对比（TS-TCC）的无监督时间序列表示学习框架，该框架可从未标记数据中学习时间序列表示，其中包括弱和强扩充转换的两个不同但相关的视图，并提出了一种新的时间对比模块和一个上下文对比模块来学习稳健的时间表示和判别性表示。