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The trend of time series characterizes the intermediate upward and downward behaviour of time series. Learning and forecasting the trend in time series data play an important role in many real applications, ranging from resource allocation in data centers, load schedule in smart grid, and so on. Inspired by the recent successes of neural networks, in this paper we propose TreNet, a novel end-to-end hybrid neural network to learn local and global contextual features for predicting the trend ofdoi:10.24963/ijcai.2017/316 dblp:conf/ijcai/LinGA17 fatcat:5cqmwviu5faszdwpus5g4xo3tq