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Satellite image sequence prediction aims to precisely infer future satellite image frames with historical observations, which is a significant and challenging dense prediction task. Though existing deep learning models deliver promising performance for satellite image sequence prediction, the methods suffer from quite expensive training costs, especially in training time and GPU memory demand, due to the inefficiently modeling for temporal variations. This issue seriously limits the lightweight application in satellites such as space-borne forecast models. In this article, we propose a lightweight prediction framework TinyPredNet for satellite image sequence prediction, in which a spatial encoder and decoder model the intra-frame appearance features and a temporal translator captures inter-frame motion patterns. To efficiently model the temporal evolution of satellite image sequences, we carefully design a multi-scale temporal-cascaded structure and a channel attention-gated structure in the temporal translator. Comprehensive experiments are conducted on FengYun-4A (FY-4A) satellite dataset, which show that the proposed framework achieves very competitive performance with much lower computation cost compared to state-of-the-art methods. In addition, corresponding interpretability experiments are conducted to show how our designed structures work. We believe the proposed method can serve as a solid lightweight baseline for satellite image sequence prediction. [ABSTRACT FROM AUTHOR]

Satellite image sequence prediction is a crucial and challenging task. Previous studies leverage optical flow methods or existing deep learning methods on spatial–temporal sequence models for the task. However, they suffer from either oversimplified model assumptions or blurry predictions and sequential error accumulation issue, for a long-term forecast requirement. In this article, we propose a novel multiscale time conditional generative adversarial network (MSTCGAN). To address the sequential error accumulation issue, MSTCGAN adopts a parallel prediction framework to produce the future image sequences by a one-hot time condition input. In addition, a powerful multiscale generator is designed with the multihead axial attention, which helps to carefully preserve the fine-grained details for appearance consistency. Moreover, we develop a temporal discriminator to address the blurry issue and maintain the motion consistency in prediction. Extensive experiments have been conducted on the FengYun-4A satellite dataset, and the results demonstrate the effectiveness and superiority of the proposed method over state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Network embedding aims to learn low-dimensional representations for nodes in social networks, which can serve many applications, such as node classification, link prediction and visualization. Most of network embedding methods focus on learning the representations solely from the topological structure. Recently, attributed network embedding, which utilizes both the topological structure and node content to jointly learn latent representations, becomes a hot topic. However, previous studies obtain the joint representations by directly concatenating the one from each aspect, which may lose the correlations between the topological structure and node content. In this paper, we propose a new attributed network embedding method, TLVANE, which can address the drawback by exploiting the deep variational autoencoders (VAEs). Particularly, a two-level VAE model is built, where the first-level accounts for the joint representations while the second for the embeddings of each aspect. Extensive experiments on three real-world datasets have been conducted, and the results demonstrate the superiority of the proposed method against state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1007/s00521-021-06724-x. B Correction to: Neural Computing and Applications (2022) 34:5905-5921 b https://doi.org/10.1007/s00521-021-06724-x The Eq. (10) in the article is corrected as below: 10 HT ht Graph Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. [Extracted from the article]

Although much research is devoted to the analysis and prediction of individuals' behavior in social networks, very few studies analyze firms' performance with respect to business networks. Empowered by recent research on the automated mining of business networks, this article illustrates the design of a novel business network-based model called the energy cascading model (ECM) for predicting directional stock price movements of related firms. More specifically, the proposed network-based predictive analytics model considers both influential business relationships and Twitter sentiments to infer a firm's middle to long-term directional stock price movements. The reported empirical experiments are based on a publicly available financial corpus and social media postings that reveal the proposed ECM model to be effective for predicting directional stock price movements. It outperforms the best baseline model, the Pearson correlation-based prediction model, in upward stock price movement prediction by 11.7 percent in terms of F-measure. [ABSTRACT FROM PUBLISHER]

As a pixel-wise dense forecast task, video prediction is challenging due to its high computation complexity, dramatic future uncertainty, and extremely complicated spatial–temporal patterns. Many deep learning methods are proposed for the task, which bring up significant improvements. However, they focus on modeling short-term spatial–temporal dynamics and fail to sufficiently exploit long-term ones. As a result, the methods tend to deliver unsatisfactory performance for a long-term forecast requirement. In this article, we propose a novel unified memory network (UNIMEMnet) for long-term video prediction, which can effectively exploit long-term motion-appearance dynamics and unify the short-term spatial–temporal dynamics and long-term ones in an architecture. In the UNIMEMnet, a dual branch multi-scale memory module is carefully designed to extract and preserve long-term spatial–temporal patterns. In addition, a short-term spatial–temporal dynamics module and an alignment and fusion module are devised to capture and coordinate short-term motion-appearance dynamics with long-term ones from our designed memory module. Extensive experiments on five video prediction datasets from both synthetic and real-world scenarios are conducted, which validate the effectiveness and superiority of our proposed method UNIMEMnet over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• We propose an interpretable partial differential network for SPLT-3D, where both horizontal and vertical interactions are modeled effectively. • We develop the theory of formulating 3D spatiotemporal dynamics with PDEs and design an effective strategy to apply it. • Experimental results show that our method can not only achieve better prediction performance, but also learn the correct PDEs. Neural network-based methods have been widely applied to spatiotemporal prediction tasks, such as video prediction and weather forecasting. However, most existing works are designed for prediction in 2D space, and 3D prediction has not been extensively studied. In this paper, we propose to leverage 3D partial differential equations (PDEs) for spatiotemporal prediction in 3D space, and further develop a novel 3D neural partial differential network. This is inspired by that 3D PDEs can model both horizontal and vertical information interactions by various partial derivatives. Moreover, they can also formulate physical knowledge by equations. To integrate 3D PDEs in neural networks, we first develop the theory of approximating 3D partial derivatives by 3D convolutions, and further present an effective strategy to utilize the theory in practice. Then based on the theory and strategy, we propose a novel 3D Neural Partial Differential Network for prediction, named NPDN-3D. Specifically, NPDN-3D consists of two pivotal modules: (1) a neural partial differential module for capturing low-order spatiotemporal dynamics. This module is the key for prediction, where the dynamics are formulated by commonly-used low-order 3D PDEs. (2) A residual module for capturing the remaining non-low-order dynamics. This module performs as an extensible plug-in to enhance the expressiveness of our model. Extensive experiments on two simulated datasets and two real datasets show that our method not only achieves better prediction but also learns the correct PDEs. [ABSTRACT FROM AUTHOR]

Traffic flow prediction (TFP) has attracted increasing attention with the development of smart city. In the past few years, neural network-based methods have shown impressive performance for TFP. However, most of previous studies fail to explicitly and effectively model the relationship between inflows and outflows. Consequently, these methods are usually uninterpretable and inaccurate. In this paper, we propose an interpretable local flow attention (LFA) mechanism for TFP, which yields three advantages. (1) LFA is flow-aware. Different from existing works, which blend inflows and outflows in the channel dimension, we explicitly exploit the correlations between flows with a novel attention mechanism. (2) LFA is interpretable. It is formulated by the truisms of traffic flow, and the learned attention weights can well explain the flow correlations. (3) LFA is efficient. Instead of using global spatial attention as in previous studies, LFA leverages the local mode. The attention query is only performed on the local related regions. This not only reduces computational cost but also avoids false attention. Based on LFA, we further develop a novel spatiotemporal cell, named LFA-ConvLSTM (LFA-based convolutional long short-term memory), to capture the complex dynamics in traffic data. Specifically, LFA-ConvLSTM consists of three parts. (1) A ConvLSTM module is utilized to learn flow-specific features. (2) An LFA module accounts for modeling the correlations between flows. (3) A feature aggregation module fuses the above two to obtain a comprehensive feature. Extensive experiments on two real-world datasets show that our method achieves a better prediction performance. We improve the RMSE metric by 3.2%–4.6%, and the MAPE metric by 6.2%–6.7%. Our LFA-ConvLSTM is also almost 32% faster than global self-attention ConvLSTM in terms of prediction time. Furthermore, we also present some visual results to analyze the learned flow correlations. [ABSTRACT FROM AUTHOR]

Regional wind speed prediction is an important spatiotemporal prediction problem which is crucial for optimizing wind power utilization. Nevertheless, the complex dynamics of wind speed pose a formidable challenge to prediction tasks. The evolving dynamics of wind could be governed by underlying physical principles that can be described by partial differential equations (PDE). This study proposes a novel approach called PDE-assisted network (PaNet) for regional wind speed prediction. In PaNet, a new architecture is devised, incorporating both PDE-based dynamics (PDE dynamics) and unknown dynamics. Specifically, this architecture establishes interactions between the two dynamics, regulated by an inter-dynamics communication unit that controls interactions through attention gates. Additionally, recognizing the significance of the initial state for PDE dynamics, an adaptive frequency-gated unit is introduced to generate a suitable initial state for the PDE dynamics by selecting essential frequency components. To evaluate the predictive performance of PaNet, this study conducts comprehensive experiments on two real-world wind speed datasets. The experimental results indicated that the proposed method is superior to other baseline methods. [ABSTRACT FROM AUTHOR]

• The SAF-Net is proposed to combine domain-knowledge and data-driven knowledge. • The spatial attention is used to automatically selected high-response wind speed area. • Extensive experiments is proceeded in the real-world dataset. A typhoon is a destructive weather system that can cause severe casualties and economic losses. Typhoon intensity (TI) is a measurement to evaluate its ruinous degree. Hence, typhoon intensity prediction is an important research problem and many methods have been proposed. However, most of the existing approaches have very limited capability to combine the 2D Typhoon Structure Domain-expert Knowledge (2D-TSDK) and the 3D Typhoon Structure Data-driven Knowledge (3D-TSDK) for the TI prediction. To address this issue, this paper proposes a spatio-temporal deep learning method named Spatial Attention Fusing Network (SAF-Net). The designed model aims to fuse the 2D-TSDK and the 3D-TSDK by developing a specific Wide & Deep framework. In the data-driven component, a special Spatial Attention (SA) module is designed to automatically select high-response wind speed areas and embedded into a three-branch CNN to exploit the 3D-TSDK. Then, the Wide & Deep framework integrates the 2D-TSDK and the 3D-TSDK for the TI prediction. Comprehensive experiments have been conducted on a real-world dataset, and the result shows that the proposed method outperforms state-of-the-art typhoon intensity prediction methods. The code is available in GitHub: https://github.com/xuguangning1218/TI_Prediction [ABSTRACT FROM AUTHOR]

The problem of effective stock trend prediction has aroused much attention these years for its profitability. The development of algorithmic trading drives explosive growth in fast and effective techniques for trend predictions. However, little attention has been paid to sample weighting schemes in this field. In this article, we propose a new sample weighting scheme for stock trend predictions based on financial features of companies. Specifically, stock trends are supposed to be determined by hidden market states represented by trend generating vectors. These vectors can be generated from financial features of companies. The scheme considers similarities between trend generating vectors (STGVs) when assigning weights to samples from different periods to differentiate their prediction capabilities. Similarity scores calculated with a proper metric are adopted to measure similarity. We train models with STGV to predict stock trends. Extensive experiments are conducted to figure out the most suitable similarity metric used in STGV and demonstrate its superiority over other sample weighting schemes together with its generalizability. • Propose the notion of trend generating vectors to represent hidden market states. • Assign weights to samples based on similarity between trend generating vectors. • Outperform other sample weighting schemes and enjoy high generalizability. [ABSTRACT FROM AUTHOR]

Reinforcement learning techniques have recently been introduced to interactive recommender systems to capture the dynamic patterns of user behavior during the interaction with recommender systems and perform planning to optimize long-term performance. Most existing research work focuses on designing policy and learning algorithms of the recommender agent but seldom cares about the state representation of the environment, which is indeed essential for the recommendation decision making. In this paper, we first formulate the interactive recommender system problem with a deep reinforcement learning recommendation framework. Within this framework, we then carefully design four state representation schemes for learning the recommendation policy. Inspired by recent advances in feature interaction modeling in user response prediction, we discover that explicitly modeling user–item interactions in state representation can largely help the recommendation policy perform effective reinforcement learning. Extensive experiments on four real-world datasets are conducted under both the offline and simulated online evaluation settings. The experimental results demonstrate the proposed state representation schemes lead to better performance over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]