Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction

Li, Ziyue; Sergin, Nurettin Dorukhan; Yan, Hao; Zhang, Chen; Tsung, Fugee

doi:10.48550/arxiv.1912.05693

Cited by 9 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Low-rank tensor decomposition and completion have been proposed as suitable approaches to anomaly detection in spatiotemporal data as these methods are a natural extension of spectral anomaly detection techniques from vector to multiway data [5], [17], [18], [3], [19], [10], [9], [20]. Most of the existing tensor based methods are supervised or semisupervised and focus on dimensionality reduction and feature extraction.…”

Section: Related Workmentioning

confidence: 99%

“…The definition of anomaly and the suitability of a particular method is determined by the application. In this paper, we focus on urban anomaly detection [7], [8], [9], [10], [11], where anomalies correspond to incidental events that occur rarely, such as irregularity in traffic volume, unexpected crowds, etc. Urban data are spatiotemporal data collected by mobile devices or distributed sensors in cities and are usually associated with timestamps and location tags.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

GLOSS: Tensor-based anomaly detection in spatiotemporal urban traffic data

Sofuoglu

Aviyente

2022

Signal Processing

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GLOSS: Tensor-based anomaly detection in spatiotemporal urban traffic data

Sofuoglu

Aviyente

2022

Signal Processing

View full text Add to dashboard Cite

“…Tensor representation. Another approach is to fold a time series matrix into a third-order tensor (sensor × time of day × day) by introducing an additional "day" dimension (e.g., [4], [12], [13]). This is a particular case for traffic data given the clear day-to-day similarity, but many real-world time series data resulted from human behavior/activities (e.g., energy/electricity consumption) also exhibit similar patterns.…”

Section: Related Workmentioning

confidence: 99%

Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation

Chen,

Lei,

Saunier

et al. 2021

Preprint

View full text Add to dashboard Cite

Spatiotemporal traffic time series (e.g., traffic volume/speed) collected from sensing systems are often incomplete with considerable corruption and large amounts of missing values, preventing users from harnessing the full power of the data. Missing data imputation has been a long-standing research topic and critical application for real-world intelligent transportation systems. A widely applied imputation method is low-rank matrix/tensor completion; however, the low-rank assumption only preserves the global structure while ignores the strong local consistency in spatiotemporal data. In this paper, we propose a low-rank autoregressive tensor completion (LATC) framework by introducing temporal variation as a new regularization term into the completion of a third-order (sensor × time of day × day) tensor. The third-order tensor structure allows us to better capture the global consistency of traffic data, such as the inherent seasonality and day-to-day similarity. To achieve local consistency, we design the temporal variation by imposing an AR(p) model for each time series with coefficients as learnable parameters. Different from previous spatial and temporal regularization schemes, the minimization of temporal variation can better characterize temporal generative mechanisms beyond local smoothness, allowing us to deal with more challenging scenarios such "blackout" missing. To solve the optimization problem in LATC, we introduce an alternating minimization scheme that estimates the low-rank tensor and autoregressive coefficients iteratively. We conduct extensive numerical experiments on several real-world traffic data sets, and our results demonstrate the effectiveness of LATC in diverse missing scenarios.Index Terms-Spatiotemporal traffic data, missing data imputation, low-rank tensor completion, truncated nuclear norm, autoregressive time series model I. INTRODUCTIONSpatiotemporal traffic data collected from various sensing systems (e.g. loop detectors and floating cars) serve as the foundation to a wide range of applications and decisionmaking processes in intelligent transportation systems. The emerging "big" data is often large-scale, high-dimensional, and incomplete, posing new challenges to modeling spatiotemporal traffic data. Missing data imputation is one of the most important research questions in spatiotemporal data analysis, since accurate and reliable imputation can help various Xinyu Chen and Nicolas Saunier are with the Civil, Geological and

show abstract

“…In fact, many real-world time series data resulted from human behavior/activities (e.g., traffic flow, customer demand, electricity consumption) exhibit both long-term and short-term patterns. Recent studies have used the tensor representation [sensor×day×time of day] to capture the patterns (e.g., [23,24,25,26]). The tensor representation also offers prediction ability by performing tensor completion [24,25].…”

Section: Related Workmentioning

confidence: 99%

“…Recent studies have used the tensor representation [sensor×day×time of day] to capture the patterns (e.g., [23,24,25,26]). The tensor representation also offers prediction ability by performing tensor completion [24,25]. The tensor representation not only preserves the dependencies among sensors but also provides a new alternative to capture both local and global temporal patterns.…”

Section: Related Workmentioning

confidence: 99%

Low-Rank Autoregressive Tensor Completion for Multivariate Time Series Forecasting

Chen,

Sun

2020

Preprint

View full text Add to dashboard Cite

Time series prediction has been a long-standing research topic and an essential application in many domains. Modern time series collected from sensor networks (e.g., energy consumption and traffic flow) are often large-scale and incomplete with considerable corruption and missing values, making it difficult to perform accurate predictions. In this paper, we propose a low-rank autoregressive tensor completion (LATC) framework to model multivariate time series data. The key of LATC is to transform the original multivariate time series matrix (e.g., sensor×time point) to a third-order tensor structure (e.g., sensor×time of day×day) by introducing an additional temporal dimension, which allows us to model the inherent rhythms and seasonality of time series as global patterns. With the tensor structure, we can transform the time series prediction and missing data imputation problems into a universal low-rank tensor completion problem. Besides minimizing tensor rank, we also integrate a novel autoregressive norm on the original matrix representation into the objective function. The two components serve different roles. The low-rank structure allows us to effectively capture the global consistency and trends across all the three dimensions (i.e., similarity among sensors, similarity of different days, and current time v.s. the same time of historical days). The autoregressive norm can better model the local temporal trends. Our numerical experiments on three real-world data sets demonstrate the superiority of the integration of global and local trends in LATC in both missing data imputation and rolling prediction tasks.Preprint. Under review.

show abstract

Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction

Cited by 9 publications

References 13 publications

GLOSS: Tensor-based anomaly detection in spatiotemporal urban traffic data

GLOSS: Tensor-based anomaly detection in spatiotemporal urban traffic data

Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation

Low-Rank Autoregressive Tensor Completion for Multivariate Time Series Forecasting

Contact Info

Product

Resources

About