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Temporal data arise in\nthese real-world applications often involves a mixture of long-term and\nshort-term patterns, for which traditional approaches such as Autoregressive\nmodels and Gaussian Process may fail. In this paper, we proposed a novel deep\nlearning framework, namely Long- and Short-term Time-series network (LSTNet),\nto address this open challenge. LSTNet uses the Convolution Neural Network\n(CNN) and the Recurrent Neural Network (RNN) to extract short-term local\ndependency patterns among variables and to discover long-term patterns for time\nseries trends. Furthermore, we leverage traditional autoregressive model to\ntackle the scale insensitive problem of the neural network model. In our\nevaluation on real-world data with complex mixtures of repetitive patterns,\nLSTNet achieved significant performance improvements over that of several\nstate-of-the-art baseline methods. 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