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Several applications of Internet of Things (IoT) technology involve capturingdata from multiple sensors resulting in multi-sensor time series. Existingneural networks based approaches for such multi-sensor or multivariate timeseries modeling assume fixed input dimension or number of sensors. Suchapproaches can struggle in the practical setting where different instances ofthe same device or equipment such as mobiles, wearables, engines, etc. comewith different combinations of installed sensors. We consider training neuralnetwork models from such multi-sensor time series, where the time series havevarying input dimensionality owing to availability or installation of adifferent subset of sensors at each source of time series. We propose a novelneural network architecture suitable for zero-shot transfer learning allowingrobust inference for multivariate time series with previously unseencombination of available dimensions or sensors at test time. Such acombinatorial generalization is achieved by conditioning the layers of a coreneural network-based time series model with a "conditioning vector" thatcarries information of the available combination of sensors for each timeseries. This conditioning vector is obtained by summarizing the set of learned"sensor embedding vectors" corresponding to the available sensors in a timeseries via a graph neural network. We evaluate the proposed approach onpublicly available activity recognition and equipment prognostics datasets, andshow that the proposed approach allows for better generalization in comparisonto a deep gated recurrent neural network baseline.

Handling Variable-Dimensional Time Series with Graph Neural Networks