Shuhan Zhong

Shuhan Zhong, Sizhe Song, Weipeng Zhuo et al.

Time series data, including univariate and multivariate ones, are characterized by unique composition and complex multi-scale temporal variations. They often require special consideration of decomposition and multi-scale modeling to analyze. Existing deep learning methods on this best fit to univariate time series only, and have not sufficiently considered sub-series modeling and decomposition completeness. To address these challenges, we propose MSD-Mixer, a Multi-Scale Decomposition MLP-Mixer, which learns to explicitly decompose and represent the input time series in its different layers. To handle the multi-scale temporal patterns and multivariate dependencies, we propose a novel temporal patching approach to model the time series as multi-scale patches, and employ MLPs to capture intra- and inter-patch variations and channel-wise correlations. In addition, we propose a novel loss function to constrain both the mean and the autocorrelation of the decomposition residual for better decomposition completeness. Through extensive experiments on various real-world datasets for five common time series analysis tasks, we demonstrate that MSD-Mixer consistently and significantly outperforms other state-of-the-art algorithms with better efficiency.

Shuhan Zhong, Weipeng Zhuo, Sizhe Song et al.

Irregular multivariate time series (IMTS) is characterized by the lack of synchronized observations across its different channels. In this paper, we point out that this channel-wise asynchrony can lead to poor channel-wise modeling of existing deep learning methods. To overcome this limitation, we propose MTM, a multi-scale token mixing transformer for the classification of IMTS. We find that the channel-wise asynchrony can be alleviated by down-sampling the time series to coarser timescales, and propose to incorporate a masked concat pooling in MTM that gradually down-samples IMTS to enhance the channel-wise attention modules. Meanwhile, we propose a novel channel-wise token mixing mechanism which proactively chooses important tokens from one channel and mixes them with other channels, to further boost the channel-wise learning of our model. Through extensive experiments on real-world datasets and comparison with state-of-the-art methods, we demonstrate that MTM consistently achieves the best performance on all the benchmarks, with improvements of up to 3.8% in AUPRC for classification.

Zhongyi Yu, Zhenghao Wu, Shuhan Zhong et al.

Missing values are a common problem that poses significant challenges to data analysis and machine learning. This problem necessitates the development of an effective imputation method to fill in the missing values accurately, thereby enhancing the overall quality and utility of the datasets. Existing imputation methods, however, fall short of explicitly considering the `missingness' information in the data during the embedding initialization stage and modeling the entangled feature and sample correlations during the learning process, thus leading to inferior performance. We propose M$^3$-Impute, which aims to explicitly leverage the missingness information and such correlations with novel masking schemes. M$^3$-Impute first models the data as a bipartite graph and uses a graph neural network to learn node embeddings, where the refined embedding initialization process directly incorporates the missingness information. They are then optimized through M$^3$-Impute's novel feature correlation unit (FRU) and sample correlation unit (SRU) that effectively captures feature and sample correlations for imputation. Experiment results on 25 benchmark datasets under three different missingness settings show the effectiveness of M$^3$-Impute by achieving 20 best and 4 second-best MAE scores on average.

Guanyao Li, Siyan Hu, Shuhan Zhong et al.

Covid-19 is primarily spread through contact with the virus which may survive on surfaces with lifespan of more than hours. To curb its spread, it is hence of vital importance to detect and quarantine those who have been in contact with the virus for sustained period of time, the so-called close contacts. In this work, we study, for the first time, automatic contact detection when the virus has a lifespan. Leveraging upon the ubiquity of WiFi signals, we propose a novel, private, and fully distributed WiFi-based approach called vContact. Users installing an app continuously scan WiFi and store its hashed IDs. Given a confirmed case, the signals of the major places he/she visited are then uploaded to a server and matched with the stored signals of users to detect contact. vContact is not based on phone pairing, and no information of any other users is stored locally. The confirmed case does not need to have installed the app for it to work properly. As WiFi data are sampled sporadically, we propose efficient signal processing approaches and similarity metric to align and match signals of any time. We conduct extensive indoor and outdoor experiments to evaluate the performance of vContact. Our results demonstrate that vContact is efficient and robust for contact detection. The precision and recall of contact detection are high (in the range of 50-90%) for close contact proximity (2m). Its performance is robust with respect to signal lengths (AP numbers) and phone heterogeneity. By implementing vContact as an app, we present a case study to demonstrate the validity of our design in notifying its users their exposure to virus with lifespan.