Nec Corporation

Yao Qin

Haifeng Chen

Guofei Jiang

Dongjin Song

Systems and methods for time series prediction are described. The systems and methods include encoding driving series into encoded hidden states, the encoding including adaptively prioritizing driving series at each timestamp using input attention, the driving series including data sequences collected from sensors. The systems and methods further includes decoding the encoded hidden states to generate a predicting model, the decoding including adaptively prioritizing encoded hidden states using temporal attention. The systems and methods further include generating predictions of future events using the predicting model based on the data sequences. The systems and methods further include generating signals for initiating an action to devices based on the predictions.

Dual stage attention based recurrent neural network for time series prediction

Systems and devices including an imaging sensor to capture video sequences in an environment having safety concerns therein. The systems and devices further including a processor to generate driving series based on observations from the video sequences, and generate predictions of future events based on the observations using a dual-stage attention-based recurrent neural network (DA-RNN). The DA-RNN includes an input attention mechanism to extract relevant driving series, an encoder to encode the extracted relevant driving series into hidden states, a temporal attention mechanism to extract relevant hidden states, and a decoder to decode the relevant hidden states. The processor further generates a signal for initiating an action to machines to mitigate harm to items.

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Princeton, NJ, United States of America

Yao Qin