Time series forecasting is a crucial component of many important applications, ranging from forecasting the stock markets to energy load prediction. The high-dimensionality, velocity and variety of the data collected in these applications pose significant and unique challenges that must be carefully addressed for each of them. In this work, a novel Temporal Logistic Neural Bag-of-Features approach, that can be used to tackle these challenges, is proposed. The proposed method can be effectively combined with deep neural networks, leading to powerful deep learning models for time series analysis. However, combining existing BoF formulations with deep feature extractors pose significant challenges: the distribution of the input features is not stationary, tuning the hyper-parameters of the model can be especially difficult and the normalizations involved in the BoF model can cause significant instabilities during the training process. The proposed method is capable of overcoming these limitations by a employing a novel adaptive scaling mechanism and replacing the classical Gaussian-based density estimation involved in the regular BoF model with a logistic kernel. The effectiveness of the proposed approach is demonstrated using extensive experiments on a large-scale financial time series dataset that consists of more than 4 million limit orders.
In contrast with @cite_3 , in this work a logistic Neural BoF formulation is used. This allows for training temporal BoF models without using any sophisticated initialization schemes and or carefully tuning any hyper-parameter, e.g., the initial scaling factor of the kernel function that was employed in @cite_3 . Furthermore, in this work, we studied behavior of the BoF model when combined with deep feature extractors and we appropriately designed an adaptive scaling method that allows for the smooth flow of information in deep BoF-based architectures. To the best of our knowledge, this is the first work in which a deep temporal formulation of the BoF model is used with deep feature extraction layers, after appropriately adapting it to the needs of the specific application, demonstrating that it is indeed possible to learn powerful deep learning models for time series analysis that outperform other competitive state-of-the-art methods.
@cite_3
2896419919 2766117736 2963828468 2892998444
Time-series forecasting has various applications in a wide range of domains, e.g., forecasting stock markets using limit order book data. Limit order book data provide much richer information about the behavior of stocks than its price alone, but also bear several challenges, such as dealing with multiple price depths and processing very large amounts of data of high dimensionality, velocity, and variety. A well-known approach for efficiently handling large amounts of high-dimensional data is the bag-of-features (BoF) model. However, the BoF method was designed to handle multimedia data such as images. In this paper, a novel temporal-aware neural BoF model is proposed tailored to the needs of time-series forecasting using high frequency limit order book data. Two separate sets of radial basis function and accumulation layers are used in the temporal BoF to capture both the short-term behavior and the long-term dynamics of time series. This allows for modeling complex temporal phenomena that occur in time-series data and further increase the forecasting ability of the model. Any other neural layer, such as feature transformation layers, or classifiers, such as multilayer perceptrons, can be combined with the proposed deep learning approach, which can be trained end-to-end using the back-propagation algorithm. The effectiveness of the proposed method is validated using a large-scale limit order book dataset, containing over 4.5 million limit orders, and it is demonstrated that it greatly outperforms all the other evaluated methods. Classification of time-series data is a challenging problem with many real-world applications, ranging from identifying medical conditions from electroencephalography (EEG) measurements to forecasting the stock market. The well known Bag-of-Features (BoF) model was recently adapted towards time-series representation. In this work, a neural generalization of the BoF model, composed of an RBF layer and an accumulation layer, is proposed as a neural layer that receives the features extracted from a time-series and gradually builds its representation. The proposed method can be combined with any other layer or classifier, such as fully connected layers or feature transformation layers, to form deep neural networks for time-series classification. The resulting networks are end-to-end differentiable and they can be trained using regular back-propagation. It is demonstrated, using two time-series datasets, including a large-scale financial dataset, that the proposed approach can significantly increase the classification metrics over other baseline and state-of-the-art techniques. Convolutional Neural Networks (CNNs) are well established models capable of achieving state-of-the-art classification accuracy for various computer vision tasks. However, they are becoming increasingly larger, using millions of parameters, while they are restricted to handling images of fixed size. In this paper, a quantization-based approach, inspired from the well-known Bag-of-Features model, is proposed to overcome these limitations. The proposed approach, called Convolutional BoF (CBoF), uses RBF neurons to quantize the information extracted from the convolutional layers and it is able to natively classify images of various sizes as well as to significantly reduce the number of parameters in the network. In contrast to other global pooling operators and CNN compression techniques the proposed method utilizes a trainable pooling layer that it is end-to-end differentiable, allowing the network to be trained using regular back-propagation and to achieve greater distribution shift invariance than competitive methods. The ability of the proposed method to reduce the parameters of the network and increase the classification accuracy over other state-of-the-art techniques is demonstrated using three image datasets. The performance of single image super-resolution has achieved significant improvement by utilizing deep convolutional neural networks (CNNs). The features in deep CNN contain different types of information which make different contributions to image reconstruction. However, most CNN-based models lack discriminative ability for different types of information and deal with them equally, which results in the representational capacity of the models being limited. On the other hand, as the depth of neural networks grows, the long-term information coming from preceding layers is easy to be weaken or lost in late layers, which is adverse to super-resolving image. To capture more informative features and maintain long-term information for image super-resolution, we propose a channel-wise and spatial feature modulation (CSFM) network in which a sequence of feature-modulation memory (FMM) modules is cascaded with a densely connected structure to transform low-resolution features to high informative features. In each FMM module, we construct a set of channel-wise and spatial attention residual (CSAR) blocks and stack them in a chain structure to dynamically modulate multi-level features in a global-and-local manner. This feature modulation strategy enables the high contribution information to be enhanced and the redundant information to be suppressed. Meanwhile, for long-term information persistence, a gated fusion (GF) node is attached at the end of the FMM module to adaptively fuse hierarchical features and distill more effective information via the dense skip connections and the gating mechanism. Extensive quantitative and qualitative evaluations on benchmark datasets illustrate the superiority of our proposed method over the state-of-the-art methods.