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Time series is prevalent in the IoT environment and used for monitoring the evolving behavior of involved entities or objects over time. Analyzing and mining such time series data serve for revealing insightful long-term and instantaneous information behind the data, e.g., trend, event, correlation and causality and so on.
Inspired by the recent successes of neural networks, in this talk we present a novel end-to-end hybrid neural network for learning the local and global contextual features of time series. In particular, we explore the idea of hybrid neural networks in a specific time series learning problem, namely learning the local trend of time series. Local trends of time series characterize the intermediate upward and downward patterns of time series. Learning and forecasting the local trend in time series data play an important role in many real applications, ranging from investing in the stock market, resource allocation in data centers and load schedule in the smart grid. We propose TreNet, a hybrid neural network which leverages convolutional neural networks (CNNs) to extract salient features from local raw data of time series and a long-short term memory recurrent neural network (LSTM) to capture such dependency of local trends. Preliminary experimental results on real datasets demonstrate the superiority of TreNet over conventional CNN, LSTM, HMM method and various kernel based baselines.