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Time series prediction has been widely applied to the finance industry in applications such as stock market price and commodity price forecasting. Machine learning methods have been widely used in financial time series prediction in recent years. How to label financial time series data to determine the prediction accuracy of machine learning models and subsequently determine final investment returns is a hot topic. Existing labeling methods of financial time series mainly label data by comparing the current data with those of a short time period in the future. However, financial time series data are typically non-linear with obvious short-term randomness. Therefore, these labeling methods have not captured the continuous trend features of financial time series data, leading to a difference between their labeling results and real market trends. In this paper, a new labeling method called "continuous trend labeling" is proposed to address the above problem. In the feature preprocessing stage, this paper proposed a new method that can avoid the problem of look-ahead bias in traditional data standardization or normalization processes. Then, a detailed logical explanation was given, the definition of continuous trend labeling was proposed and also an automatic labeling algorithm was given to extract the continuous trend features of financial time series data. Experiments on the Shanghai Composite Index and Shenzhen Component Index and some stocks of China showed that our labeling method is a much better state-of-the-art labeling method in terms of classification accuracy and some other classification evaluation metrics. The results of the paper also proved that deep learning models such as LSTM and GRU are more suitable for dealing with the prediction of financial time series data. [ABSTRACT FROM AUTHOR]

We develop a combinatorial approach to represent and infer semantic interpretations of video contents using tools from Grenander’s pattern theory. Semantic structures for video interpretation are formed using generators and bonds, the fundamental units of representation in pattern theory. Generators represent features and ontological items, such as actions and objects, whereas bonds are threads used to connect generators while respecting appropriate constraints. The resulting configurations of partially-connected generators are termed scene interpretations. Our goal is to parse a given video data set into high-probability configurations. The probabilistic models are imposed using energies that have contributions from both data (classification scores) and prior information (ontological constraints, co-occurrence frequencies, etc). The search for optimal configurations is based on an MCMC, simulated-annealing algorithm that uses simple moves to propose configuration changes and to accept/reject them according to the posterior energy. In contrast to current graphical methods, this framework does not preselect a neighborhood structure but tries to infer it from the data. The proposed framework is able to obtain 20% higher classification rates, compared to a purely machine learning-based baseline, despite artificial insertion of low-level processing errors. In an uncontrolled scenario, video interpretation performance rates are found to be double that of the baseline. [ABSTRACT FROM AUTHOR]

• We propose a novel multi-view DCNN-based approach which can take advantage of not only multi-view images but also the relationships between them. • The proposed networks learn the integrated features of multi-view fundus images for DR detection. • We introduce the attention mechanisms for mining the relationship between different views. • In order to boost the ability to capture the tiny lesion features in the retinal images, we combine the idea of attention mechanisms with the channel dimension. Diabetic retinopathy (DR) detection has attracted much attention recently, and the deep learning algorithms have gained traction in this area. At present, DR screening by deep learning algorithms is often based on single-view fundus images, which usually leads to an unsatisfactory accuracy of DR grading due to the incomplete lesion features. In this paper, we proposed a novel diabetic retinopathy detection convolutional network for automatic DR detection by integrating multi-view fundus images. Compared to existing single-view DCNN-based DR detection methods, the proposed method has the following advantages. First, our method fully utilizes the lesion features from the retina with a field-of-view around 120 ∘ − 150 ∘. Second, by introducing the attention mechanisms, more attention will be paid on the influential view and the performance can be improved. Besides, we also assign large weights to important channels in the network for effective feature extraction. Experiments are conducted on our collected multi-view DR dataset contained 15,468 images, in which each eye sample provides four-view images. The experimental results indicate that using multi-view images is suitable for automatic DR detection and our proposed method is superior to other benchmarking methods. [ABSTRACT FROM AUTHOR]