Learning to Reconstruct Missing Data from Spatiotemporal Graphs with
Sparse Observations
- URL: http://arxiv.org/abs/2205.13479v1
- Date: Thu, 26 May 2022 16:40:48 GMT
- Title: Learning to Reconstruct Missing Data from Spatiotemporal Graphs with
Sparse Observations
- Authors: Ivan Marisca, Andrea Cini, Cesare Alippi
- Abstract summary: This paper tackles the problem of learning effective models to reconstruct missing data points.
We propose a class of attention-based architectures, that given a set of highly sparse observations, learn a representation for points in time and space.
Compared to the state of the art, our model handles sparse data without propagating prediction errors or requiring a bidirectional model to encode forward and backward time dependencies.
- Score: 11.486068333583216
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Modeling multivariate time series as temporal signals over a (possibly
dynamic) graph is an effective representational framework that allows for
developing models for time series analysis. In fact, discrete sequences of
graphs can be processed by autoregressive graph neural networks to recursively
learn representations at each discrete point in time and space. Spatiotemporal
graphs are often highly sparse, with time series characterized by multiple,
concurrent, and even long sequences of missing data, e.g., due to the
unreliable underlying sensor network. In this context, autoregressive models
can be brittle and exhibit unstable learning dynamics. The objective of this
paper is, then, to tackle the problem of learning effective models to
reconstruct, i.e., impute, missing data points by conditioning the
reconstruction only on the available observations. In particular, we propose a
novel class of attention-based architectures that, given a set of highly sparse
discrete observations, learn a representation for points in time and space by
exploiting a spatiotemporal diffusion architecture aligned with the imputation
task. Representations are trained end-to-end to reconstruct observations w.r.t.
the corresponding sensor and its neighboring nodes. Compared to the state of
the art, our model handles sparse data without propagating prediction errors or
requiring a bidirectional model to encode forward and backward time
dependencies. Empirical results on representative benchmarks show the
effectiveness of the proposed method.
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