Human mobility is crucial for urban planning (e.g., public transportation) and epidemic response strategies. However, existing research often neglects integrating comprehensive perspectives on spatial dynamics, temporal trends, and other contextual views due to the limitations of existing mobility datasets. To bridge this gap, we introduce MOBINS (MOBIlity Networked time Series), a novel dataset collection designed for networked time-series forecasting of dynamic human movements. MOBINS features diverse and explainable datasets that capture various mobility patterns across different transportation modes in four cities and two countries and cover both transportation and epidemic domains at the administrative area level. Our experiments with nine baseline methods reveal the significant impact of different model backbones on the proposed six datasets. We provide a valuable resource for advancing urban mobility research.

Time-series unsupervised domain adaptation (TS-UDA) is essential in fields such as healthcare and manufacturing, where data often consists of distinct entities, such as individual patients or machinery.This heterogeneity leads to discrepancies not only in feature distributions but also in label distributions, posing a significant challenge for domain adaptation.However, prior studies have mostly focused on alleviating covariate shifts, resulting in predicted target labels that are often biased toward the source domain's label distribution.To address this issue, we propose Time-series domain Adaptation for mitigating Label Shifts (TA4LS), a novel label refinement approach.TA4LS leverages the consistency between predicted labels and clustering information obtained from the unique characteristics that differentiate each label in the target domain.Furthermore, our approach as a plug-in module achieves performance improvements across diverse existing unsupervised domain adaptation methods, particularly in scenarios with significant discrepancies between source and target label distributions.In experiments on four benchmark datasets with label shifts, TA4LS demonstrates superior performance across six unsupervised domain adaptation methods and six label shift handling modules.

Understanding human mobility patterns is a complex challenge that requires modeling both node-oriented time series (e.g., population) and edge-oriented time series (e.g., population flows) within graph topologies across time.While previous methods have focused on either node-oriented time series or interactions, the synergistic integration of these two modalities has proven difficult to achieve.In this paper, we propose BINTS (BI-modal learning for Networked Time Series), a novel bi-modal learning framework that employs soft contrastive learning along the temporal axis.BINTS captures modality similarities and temporal patterns by simultaneously learning from evolving node-oriented time series and interactions, solving the limitations of single-modality approaches.To evaluate our method, we curate comprehensive multi-modal human mobility datasets spanning diverse locations and times.Our experimental results demonstrate that BINTS significantly outperforms existing forecasting models by capturing synergies across different data modalities.Overall, we establish BINTS as a powerful technique for holistically understanding and forecasting complex mobility dynamics.For reproducibility, the source code of our framework is available at https://github.com/kaist-dmlab/BINTS.

Although time-series classification has many applications in healthcare and manufacturing, the high cost of data collection and labeling hinders its widespread use. To reduce data collection and labeling costs while maintaining high classification accuracy, we propose a novel problem setting, called semi-supervised learning with low-sampling-rate time series, in which the majority of time series are collected at a low sampling rate and are unlabeled whereas the minority of time series are collected at a high sampling rate and are labeled. For this novel problem scenario, we develop the SemiTSR framework equipped with the super-resolution module and the semi-supervised learning module. Here, low-sampling-rate time series are upsampled precisely, taking periodicity and trend at each timestamp into account, and both labeled and unlabeled high-sampling-rate time series are utilized for training. In particular, consistency regularization between artificially downsampled time series derived from an original high-sampling-rate time series is effective at overcoming limited sampling rates. We demonstrate that SemiTSR significantly outperforms conventional semi-supervised learning techniques by assuring high classification accuracy with low-sampling-rate time series.

In light of the remarkable advancements made in time-series anomaly detection(TSAD), recent emphasis has been placed on exploiting the frequency domain as well as the time domain to address the difficulties in precisely detecting pattern-wise anomalies. However, in terms of anomaly scores, the window granularity of the frequency domain is inherently distinct from the data-point granularity of the time domain. Owing to this discrepancy, the anomaly information in the frequency domain has not been utilized to its full potential for TSAD. In this paper, we propose a TSAD framework, Dual-TF, that simultaneously uses both the time and frequency domains while breaking the time-frequency granularity discrepancy. To this end, our framework employs nested-sliding windows, with the outer and inner windows responsible for the time and frequency domains, respectively, and aligns the anomaly scores of the two domains. As a result of the high resolution of the aligned scores, the boundaries of pattern-wise anomalies can be identified more precisely. In six benchmark datasets, our framework outperforms state-of-the-art methods by 12.0--147%, as demonstrated by experimental results.

The escalating crisis of COVID-19 has put people all over the world in danger. Owing to the high contagion rate of the virus, COVID-19 cases continue to increase globally. To further suppress the threat of the COVID-19 pandemic and minimize its damage, it is imperative that each country monitors inbound travelers. Moreover, given that resources for quarantine are often limited, they must be carefully allocated. In this paper, to aid in such allocation by predicting the number of inbound COVID-19 cases, we propose Hi-COVIDNet, which takes advantage of the geographic hierarchy. Hi-COVIDNet is based on a neural network with two-level components, namely, country-level and continent-level encoders, which understand the complex relationships among foreign countries and derive their respective contagion risk to the destination country. An in-depth case study in South Korea with real-world COVID-19 datasets confirmed the effectiveness and practicality of Hi-COVIDNet.