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Kullback-Leibler Divergence. Everything from phase of the moon to the price of gold and a hundred indexes. The US National Center for Education Statistics : Data on educational institutions and education demographics from the US and around the world. For example, Ding.

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Unlike conventional RNN, it is well-suited to learn from experience to predict time series when there are time steps with arbitrary size. Moreover, to get stronger evidence, we further test the long-term relationships between the six stock indices and their corresponding index futures. Profitability test The results of profitability test are shown in Table. Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval. It is also by far the largest stock exchange and the most efficient market in the world. The proposed deep forex fx foreign exchange risk it is learning framework, wsaes-lstm, can extract more abstract and invariant features compared with the traditional long-short term memory and recurrent neural networks (RNN) approaches. The model learns a hidden feature a ( x ) from input x by reconstructing it on x'. Neural network approach to forecasting of quasiperiodic financial time series. Greedy Layer-Wise Training of Deep Networks.