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One of challenging tasks in the field of artificial intelligence is the human action recognition. In this paper, we propose a novel long-term temporal feature learning architecture for recognizing human action in video, named Pseudo Recurrent Residual Neural Networks (P-RRNNs), which exploits the recurrent architecture and composes each in different connection among units. Two-stream CNNs model (GoogLeNet) is employed for extracting local temporal and spatial features respectively. The local spatial and temporal features are then integrated into global long-term temporal features by using our proposed two-stream P-RRNNs. Finally, the Softmax layer fuses the outputs of two-stream P-RRNNs for action recognition. The experimental results on two standard databases UCF101 and HMDB51 demonstrate the outstanding performance of proposed method based on architectures for human action recognition. INDEX TERMS Action recognition, residual learning, recurrent neural networks, long short-term memory (LSTM).

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Human action recognition is an important and challenging topic in computer vision. Recently, convolutional neural networks (CNNs) have established impressive results for many image recognition tasks. The CNNs usually contain million parameters which prone to overfit when training on small datasets. Therefore, the CNNs do not produce superior performance over traditional methods for action recognition. In this study, the authors design a novel two-stream fully convolutional networks architecture for action recognition which can significantly reduce parameters while keeping performance. To utilise the advantage of spatial-temporal features, a linear weighted fusion method is used to fuse two-stream networks' feature maps and a video pooling method is adopted to construct the video-level features. At the meantime, the authors also demonstrate that the improved dense trajectories has significant impact for action recognition. The authors' method can achieve the state-of-the-art performance on two challenging datasets UCF101 (93.0%) and HMDB51 (70.2%).

Due to the volatility and noise of the stock market, accurately obtaining the trend of the stock market is a challenging problem, and gets the attention of many researchers and speculators. Recently, convolutional neural network (CNN) has been used to automatically learn effective features and predict stock market trends. In CNN-based methods reported so far, less focus has been paid to time series information of the stock, but is very crucial for stock forecasting. In this study, an unsupervised feature extraction method with convolutional autoencoder (CAE) with application to daily stock market prediction is proposed, which has a higher prediction than traditional models.The proposed method mainly consists of the data processing part, unsupervised feature learning part, and the support vector machine model part. Data processing part includes time series data transform into two-dimensional data and data normalization. CAE network-based unsupervised feature learning is designed by fusing convolution and autoencoder. In order to verify the performance of the model, various initial financial and economic variables of stock indices are chosen for prediction experiments. The experimental results on different stock indices demonstrate a significant improvement in prediction's performance compared with the baseline methods. K E Y W O R D S convolutional autoencoder network, convolutional neural network, stock market prediction, support vector machine INTRODUCTIONThe stock market has become an important component for listed companies to raise funds from investors. However, the law of stock price change is difficult to accurately grasp, and most investors usually depend on subjective judgment to conduct stock trading. 1,2 The stock market has always been affected by political factors, the industry specific factors, the world economic situation, and the price trends are highly nonlinear and nonstationary. [3][4][5] Therefore, effective and efficient predicting the stock is an extremely challenging task for both investors and researchers. And thus, it is significant to research how to build a universal and effective model to forecast future movements of the stock market.There are a number of advanced methods that are applied for prediction of the stock market. Depending on the relationship between the historical behavior and future trend of the stock price is processed, forecasting methods can be divided into three categories. [6][7][8] Among statistical methodologies, due to the interpretability, linear regression, autoregression, and moving average are helpful in financial time series forecasting. Assume that the stock price trend is a result of historical behaviors, extracting effective features from the original stock data is the key of the prediction process. However, features are subjectively designed, and the accurate prediction of models mostly depended on the A preliminary version of this article has been published by the seventh International Conference on Information Science and Control Engineering.

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