Tata Consultancy Services Limited

Jyoti Narwariya

Gautam Shroff

Lovekesh Vig

Pankaj Malhotra

Vibhor Gupta

Vishnu Tankasala Veparala

Several applications capture data from sensors resulting in multi-sensor time series. Existing neural networks-based approaches for such multi-sensor/multivariate time series modeling assume fixed input-dimension/number of sensors. Such approaches can struggle in practical setting where different instances of same device/equipment come with different combinations of installed sensors. In the present disclosure, neural network models are trained from such multi-sensor time series having varying input dimensionality, owing to availability/installation of different sensors subset at each source of time series. Neural network (NN) architecture is provided for zero-shot transfer learning allowing robust inference for multivariate time series with previously unseen combination of available dimensions/sensors at test time. Such combinatorial generalization is achieved by conditioning layers of core NN-based time series model with 'conditioning vector' carrying information of available sensors combination for each time series and is obtained by summarizing learned “sensor embedding vectors set” corresponding to available sensors in time series.

Neural networks for handling variable-dimensional time series data

Neural networks can be used for time series data classification. However, in a K-shot scenario in which sufficient training data is unavailable to train the neural network, the neural network may not produce desired results. Disclosed herein are a method and system for training a neural network for time series data classification. In this method, by processing a plurality of task specific data, a system generates a set of updated parameters, which is further used to train a neural network (network) till a triplet loss is below a threshold. The network is trained on a diverse set of few-shot tasks sampled from various domains (e.g. healthcare, activity recognition, and so on) such that it can solve a target task from another domain using only a small number of training samples from the target task.

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Noida, India

Jyoti Narwariya