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Chengsen Wang, Qi Qi, Jingyu Wang, Haifeng Sun, Zirui Zhuang, Jinming Wu, Lei Zhang, Jianxin Liao

Human experts typically integrate numerical and textual multimodal information to analyze time series. However, most traditional deep learning predictors rely solely on unimodal numerical data, using a fixed-length window for training and prediction on a single dataset, and cannot adapt to different scenarios. The powered pre-trained large language model has introduced new opportunities for time series analysis. Yet, existing methods are either inefficient in training, incapable of handling textual information, or lack zero-shot forecasting capability. In this paper, we innovatively model time series as a foreign language and construct ChatTime, a unified framework for time series and text processing. As an out-of-the-box multimodal time series foundation model, ChatTime provides zero-shot forecasting capability and supports bimodal input/output for both time series and text. We design a series of experiments to verify the superior performance of ChatTime across multiple tasks and scenarios, and create four multimodal datasets to address data gaps. The experimental results demonstrate the potential and utility of ChatTime.

Peiwang Tang, Weitai Zhang

Recent studies have attempted to refine the Transformer architecture to demonstrate its effectiveness in Long-Term Time Series Forecasting (LTSF) tasks. Despite surpassing many linear forecasting models with ever-improving performance, we remain skeptical of Transformers as a solution for LTSF. We attribute the effectiveness of these models largely to the adopted Patch mechanism, which enhances sequence locality to an extent yet fails to fully address the loss of temporal information inherent to the permutation-invariant self-attention mechanism. Further investigation suggests that simple linear layers augmented with the Patch mechanism may outperform complex Transformer-based LTSF models. Moreover, diverging from models that use channel independence, our research underscores the importance of cross-variable interactions in enhancing the performance of multivariate time series forecasting. The interaction information between variables is highly valuable but has been misapplied in past studies, leading to suboptimal cross-variable models. Based on these insights, we propose a novel and simple Patch-based MLP (PatchMLP) for LTSF tasks. Specifically, we employ simple moving averages to extract smooth components and noise-containing residuals from time series data, engaging in semantic information interchange through channel mixing and specializing in random noise with channel independence processing. The PatchMLP model consistently achieves state-of-the-art results on several real-world datasets. We hope this surprising finding will spur new research directions in the LTSF field and pave the way for more efficient and concise solutions.

Liaoyuan Tang, Zheng Wang, Jie Wang, Guanxiong He, Zhezheng Hao, Rong Wang, Feiping Nie

The representation learning of time series has a wide range of downstream tasks and applications in many practical scenarios. However, due to the complexity, spatiotemporality, and continuity of sequential stream data, compared with the representation learning of structural data such as images/videos, the time series self-supervised representation learning is even more challenging. Besides, the direct application of existing contrastive learning and masked autoencoder based approaches to time series representation learning encounters inherent theoretical limitations, such as ineffective augmentation and masking strategies. To this end, we propose a Language Pre-training guided Masking Representation Learning (LPMRL) for times series classification. Specifically, we first propose a novel language pre-training guided masking encoder for adaptively sampling semantic spatiotemporal patches via natural language descriptions and improving the discriminability of latent representations. Furthermore, we present the dual-information contrastive learning mechanism to explore both local and global information by meticulously designing high-quality hard negative samples of time series data samples. As a result, we also design various experiments, such as visualization of masking position and distribution and reconstruction error to verify the reasonability of proposed language guided masking technique. Last, we evaluate the performance of proposed representation learning via classification task conducted on 106 time series datasets, which demonstrates the effectiveness of proposed method.

Ikumi Suzuki, Kazuo Hara, Eiji Murakami

This study proposes a new change detection method that leverages hubness. Hubness is a phenomenon that occurs in high-dimensional spaces, where certain special data points, known as hub data, tend to be closer to other data points. Hubness is known to degrade the accuracy of methods based on nearest neighbor search. Therefore, many studies in the past have focused on reducing hubness to improve accuracy. In contrast, this study utilizes hubness to detect changes. Specifically, if there is no change, suppressing the hubness occurring in the two datasets obtained by dividing the time series data will result in a uniform data distribution. However, if there is a change, even if we try to reduce the hubness in the two datasets obtained by dividing the time series data before and after the change, the hubness will not be reduced, and the data distribution will not become uniform. We use this finding to detect changes. Experiments with synthetic data show that the proposed method achieves accuracy comparable to or exceeding that of existing methods. Additionally, the proposed method achieves good accuracy with real-world data from hydraulic systems and gas sensors, along with excellent runtime performance.

Ziwei Liu, Qidong Liu, Yejing Wang, Wanyu Wang, Pengyue Jia, Maolin Wang, Zitao Liu, Yi Chang, Xiangyu Zhao

Sequential Recommender Systems (SRS) has stood out as a highly promising technique in numerous domains due to its impressive capability of capturing complex user preferences. Current SRS have employed transformer-based models to give the next-item prediction. Nevertheless, its quadratic computational complexity has often resulted in notable inefficiencies, posing a significant obstacle to real-time recommendation processes. Recently, Mamba has demonstrated its exceptional effectiveness in time series prediction, delivering substantial improvements in both efficiency and effectiveness. However, directly applying Mamba to SRS poses certain challenges. Its unidirectional structure may impede the ability to capture contextual information in user-item interactions, while its instability in state estimation may hinder the ability to capture short-term patterns in interaction sequences. To address these issues, we propose a novel framework called Selective Gated Mamba for Sequential Recommendation (SIGMA). By introducing the Partially Flipped Mamba (PF-Mamba), we construct a special bi-directional structure to address the context modeling challenge. Then, to consolidate PF-Mamba's performance, we employed an input-dependent Dense Selective Gate (DS Gate) to allocate the weights of the two directions and further filter the sequential information. Moreover, for short sequence modeling, we devise a Feature Extract GRU (FE-GRU) to capture the short-term dependencies. Experimental results demonstrate that SIGMA significantly outperforms existing baselines across five real-world datasets. Our implementation code is available in Supplementary Material to ease reproducibility.

Yaxuan Kong, Zepu Wang, Yuqi Nie, Tian Zhou, Stefan Zohren, Yuxuan Liang, Peng Sun, Qingsong Wen

Traditional recurrent neural network architectures, such as long short-term memory neural networks (LSTM), have historically held a prominent role in time series forecasting (TSF) tasks. While the recently introduced sLSTM for Natural Language Processing (NLP) introduces exponential gating and memory mixing that are beneficial for long term sequential learning, its potential short memory issue is a barrier to applying sLSTM directly in TSF. To address this, we propose a simple yet efficient algorithm named P-sLSTM, which is built upon sLSTM by incorporating patching and channel independence. These modifications substantially enhance sLSTM's performance in TSF, achieving state-of-the-art results. Furthermore, we provide theoretical justifications for our design, and conduct extensive comparative and analytical experiments to fully validate the efficiency and superior performance of our model.

Xinke Jiang, Wentao Zhang, Yuchen Fang, Xiaowei Gao, Hao Chen, Haoyu Zhang, Dingyi Zhuang, Jiayuan Luo

As a typical problem of Spatiotemporal Resource Management, Time Series Supplier Allocation (TSSA) poses a complex NP-hard challenge, aimed at refining future order dispatching strategies to satisfy the trade-off between demands and maximum supply. The Black-Litterman (BL) model, which comes from financial portfolio management, offers a new perspective for the TSSA by balancing expected returns against insufficient supply risks. However, the BL model is not only constrained by manually constructed perspective matrices and spatio-temporal market dynamics but also restricted by the absence of supervisory signals and unreliable supplier data. To solve these limitations, we introduce the pioneering Deep Black-Litterman Model for TSSA, which innovatively adapts the BL model from financial domain to supply chain context. Specifically, DBLM leverages Spatio-Temporal Graph Neural Networks (STGNNs) to capture spatio-temporal dependencies for automatically generating future perspective matrices. Moreover, a novel Spearman rank correlation is designed as our DBLM supervise signal to navigate complex risks and interactions of the supplier. Finally, DBLM further uses a masking mechanism to counteract the bias of unreliable data, thus improving precision and reliability. Extensive experiments on two datasets demonstrate significant improvements of DBLM on TSSA.

Mohan Gao, Kexin Xu, Xiaofeng Gao, Tengwei Cai, Haoyuan Ge

With the widely adoption of microservice architecture in the cloud computing industry, accurate prediction of workloads, especially CPU cores, can support reasonable resource allocation, thereby optimizing the resource utilization of the system. However, workload prediction is challenging in two dimensions. In the temporal dimension, workload series 1) has non-stationary characteristics, leading to poor predictability; 2) has a multi-periodic nature with entangled temporal patterns; 3) may be influenced by dynamic system states like response time and number of requests. In the spatial dimension, when regarding microservices as nodes in a distributed system, there is no topology caused by physical connections, but exists complex similarity dependencies. Extracting robust spatial features from these dependencies presents difficulties. To address these, we propose STEAM, a Spatio Temporal Heterogenous Graph Contrastive Learning for Microservice Workload Prediction. STEAM leverages non-stationary decomposition self-attention to extract temporal features from non-stationary and multi-periodic workload series, while the decoupled embedding is used to capture system state information of microservices. By treating microservices as nodes and constructing a similarity graph, STEAM effectively models the similarity relationships between microservices. To reduce the prior interference caused by the similarity threshold and improve the robustness, STEAM constructs two heterogeneous augmentation views and uses contrastive learning to extract the shared consistent spatial features. The multi-scale learning is adopted to model the long- and short-term temporal features, forming a spatio-temporal stacking structure. Experiments on two datasets, including MS dataset obtained from Ant Group, which is one of the world’s largest cloud service providers, demonstrate the superiority of STEAM.

Ren Fujiwara, Yasuko Matsubara, Yasushi Sakurai

We study the problem of modeling a non-linear dynamical system when given a time series by deriving equations directly from the data. Despite the fact that time series data are given as input, models for dynamics and estimation algorithms that incorporate long-term temporal dependencies are largely absent from existing studies. In this paper, we introduce a latent state to allow time-dependent modeling and formulate this problem as a dynamics estimation problem in latent states. We face multiple technical challenges, including (1) modeling latent non-linear dynamics and (2) solving circular dependencies caused by the presence of latent states. To tackle these challenging problems, we propose a new method, Latent Non-Linear equation modeling (LaNoLem), that can model a latent non-linear dynamical system and a novel alternating minimization algorithm for effectively estimating latent states and model parameters. In addition, we introduce criteria to control model complexity without human intervention. Compared with the state-of-the-art model, LaNoLem achieves competitive performance for estimating dynamics while outperforming other methods in prediction.

Jingru Fei, Kun Yi, Wei Fan, Qi Zhang, Zhendong Niu

We propose an energy amplification technique to address the issue that existing models easily overlook low-energy components in time series forecasting. This technique comprises an energy amplification block and an energy restoration block. The energy amplification block enhances the energy of low-energy components to improve the model's learning efficiency for these components, while the energy restoration block returns the energy to its original level. Moreover, considering that the energy-amplified data typically displays two distinct energy peaks in the frequency spectrum, we integrate the energy amplification technique with a seasonal-trend forecaster to model the temporal relationships of these two peaks independently, serving as the backbone for our proposed model, Amplifier. Additionally, we propose a semi-channel interaction temporal relationship enhancement block for Amplifier, which enhances the model's ability to capture temporal relationships from the perspective of the commonality and specificity of each channel in the data. Extensive experiments on eight time series forecasting benchmarks consistently demonstrate our model's superiority in both effectiveness and efficiency compared to state-of-the-art methods.

Yuxin Chen, Fangru Lin, Jingyi Huo, Hui Yan

Spatial-temporal graph modeling is challenging due to the diverse node interactions across spatial and temporal dimensions. Recent studies typically adopt Graph Neural Networks (GNNs) to perform node-level aggregation at different time steps, acting as a series of low-pass graph spectral filters, for node interaction modeling. However, these filters, confined to the spatial dimension, are ill-suited for processing signals of nodes with inherent spatial-temporal interdependencies. Moreover, oversimplified low-pass filtering fails to fully exploit information from diverse node interactions. To address these issues, we propose a Spatial-Temporal Spectral Graph Neural Network (STSGNN), which designs specialized two-dimensional (2-D) graph spectral filters for comprehensive spatial-temporal graph modeling. First, based on the normalized Laplacian spectrum of spatial and temporal graphs, we extend the existing graph spectral theory from a univariate spatial dimension to a bivariate spatial-temporal dimension through a 2-D Discrete Graph Fourier Transform (2-D DGFT). Then, we leverage the bivariate Bernstein polynomial approximation, with learned basis coefficients, to design 2-D filters with specialized spectral properties for unified spatial-temporal signal filtering. Finally, the filtered signals, with refined spatial-temporal representations, are fed into well-designed pyramidal gated convolution modules to acquire multiple ranges of spatial-temporal dependencies. Experiments on traffic and meteorological prediction tasks demonstrate that STSGNN achieves state-of-the-art performance. Additionally, we visualize the 2-D filters learned from inputs with distinct spatial-temporal characteristics to enhance the model's interpretability.

Nourhan Ahmed, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme

Networked time series are time series on a graph, one for each node, with applications in traffic and weather monitoring. Graph neural networks are natural candidates for networked time series imputation and have recently outperformed existing alternatives such as recurrent and generative models for time series imputation as they utilize a relational inductive bias for imputation. However, existing GNN-based approaches fail to capture the higher-order topological structure between sensors, which are shaped by recurring substructures in the graph, referred to as temporal motifs. In addition, it remains uncertain which motifs are the most pivotal motifs guiding the imputation task in networked time series. In this paper, we fill in this gap by proposing a graph neural network designed to leverage motif structures within the network by employing weighted motif adjacency matrices to capture higher-order neighborhood information. In particular, (1) we design a motif-wise multi-view attention module that explicitly captures various higher-order structures along with an attention mechanism that automatically assigns high weights to informative ones in order to maximize the use of higher-order information. (2) We introduce a gated fusion module by merging gated recurrent networks and graph convolutional networks to capture the spatial and temporal dependency in order to reflect the intricate impacts of temporal and spatial influence. Experimental results demonstrate that when compared to state-of-the-art models for time-series imputation tasks, our proposed model can reduce the error by around 19%.

Bochao Zou, Zizheng Guo, Xiaocheng Hu, Huimin Ma

Remote photoplethysmography (rPPG) is a method for non-contact measurement of physiological signals from facial videos, holding great potential in various applications such as healthcare, affective computing, and anti-spoofing. Existing deep learning methods struggle to address two core issues of rPPG simultaneously: understanding the periodic pattern of rPPG among long contexts and addressing large spatiotemporal redundancy in video segments. These represent a trade-off between computational complexity and the ability to capture long-range dependencies. In this paper, we introduce RhythmMamba, a state space model-based method that captures long-range dependencies while maintaining linear complexity. By viewing rPPG as a time series task through the proposed frame stem, the periodic variations in pulse waves are modeled as state transitions. Additionally, we design multi-temporal constraint and frequency domain feed-forward, both aligned with the characteristics of rPPG time series, to improve the learning capacity of Mamba for rPPG signals. Extensive experiments show that RhythmMamba achieves state-of-the-art performance with 319% throughput and 23% peak GPU memory.

Zeyu Wang, Chen Li, Huiying Xu, Xinzhong Zhu, Hongbo Li

Driven by the rapid development of deep learning technology, the YOLO series has set a new benchmark for real-time object detectors. Additionally, transformer-based structures have emerged as the most powerful solution in the field, greatly extending the model's receptive field and achieving significant performance improvements. However, this improvement comes at a cost, as the quadratic complexity of the self-attentive mechanism increases the computational burden of the model. To address this problem, we introduce a simple yet effective baseline approach called Mamba YOLO. Our contributions are as follows: 1) We propose that the ODMamba backbone introduce a State Space Model (SSM) with linear complexity to address the quadratic complexity of self-attention. Unlike the other Transformer-base and SSM-base method, ODMamba is simple to train without pretraining. 2) For real-time requirement, we designed the macro structure of ODMamba, determined the optimal stage ratio and scaling size. 3) We design the RG Block that employs a multi-branch structure to model the channel dimensions, which addresses the possible limitations of SSM in sequence modeling, such as insufficient receptive fields and weak image localization. This design captures localized image dependencies more accurately and significantly. Extensive experiments on the publicly available COCO benchmark dataset show that Mamba YOLO achieves state-of-the-art performance compared to previous methods. Specifically, a tiny version of Mamba YOLO achieves a 7.5% improvement in mAP on a single 4090 GPU with an inference time of 1.5 ms.

Changshuo Wang, Shuting He, Xiang Fang, Meiqing Wu, Siew-Kei Lam, Prayag Tiwari

Few-shot point cloud semantic segmentation aims to accurately segment "unseen" new categories in point cloud scenes using limited labeled data. However, pretraining-based methods not only introduce excessive time overhead but also overlook the local structure representation among irregular point clouds. To address these issues, we propose a pretraining-free local structure fitting network for few-shot point cloud semantic segmentation, named TaylorSeg. Specifically, inspired by Taylor series, we treat the local structure representation of irregular point clouds as a polynomial fitting problem and propose a novel local structure fitting convolution, called TaylorConv. This convolution learns the low-order basic information and high-order refined information of point clouds from explicit encoding of local geometric structures. Then, using TaylorConv as the basic component, we construct two variant of TaylorSeg: a non-parametric TaylorSeg-NN and a parametric TaylorSeg-PN. The former can achieve performance comparable to existing parametric models without pretraining. For the latter, we equip it with an Adaptive Push-Pull (APP) module to mitigate the feature distribution differences between the query set and the support set. Extensive experiments validate the effectiveness of the proposed method. Notably, under the 2-way 1-shot setting, TaylorSeg-PN achieves improvements of +2.28% and +4.37% mIoU on the S3DIS and ScanNet datasets respectively, compared to the previous state-of-the-art methods.

Delong Liu, Zhaohui Hou, Mingjie Zhan, Shihao Han, Zhicheng Zhao, Fei Su

Recently, diffusion-based video generation models have achieved significant success. However, existing models often suffer from issues like weak consistency and declining image quality over time. To overcome these challenges, inspired by aesthetic principles, we propose a non-invasive plug-in called Uniform Frame Organizer (UFO), which is compatible with any diffusion-based video generation model. The UFO comprises a series of adaptive adapters with adjustable intensities, which can significantly enhance the consistency between the foreground and background of videos and improve image quality without altering the original model parameters when integrated. The training for UFO is simple, efficient, requires minimal resources, and supports stylized training. Its modular design allows for the combination of multiple UFOs, enabling the customization of personalized video generation models. Furthermore, the UFO also supports direct transferability across different models of the same specification without the need for specific retraining. The experimental results indicate that UFO effectively enhances video generation quality and demonstrates its superiority in public video generation benchmarks.

Ruoran Li, Runzhao Yang, Wenxin Xiang, Yuxiao Cheng, Tingxiong Xiao, Lu Yang, Jinli Suo

Functional Magnetic Resonance Imaging (fMRI) data is a widely used kind of four-dimensional biomedical data, which requires effective compression. However, fMRI compressing poses unique challenges due to its intricate temporal dynamics, low signal-to-noise ratio, and complicated underlying redundancies. This paper reports a novel compression paradigm specifically tailored for fMRI data based on Implicit Neural Representation (INR). The proposed approach focuses on removing the various redundancies among the time series by employing several methods, including (i) conducting spatial correlation modeling for intra-region dynamics, (ii) decomposing reusable neuronal activation patterns, and (iii) using proper initialization together with nonlinear fusion to describe the inter-region similarity. This scheme appropriately incorporates the unique features of fMRI data, and experimental results on publicly available datasets demonstrate the effectiveness of the proposed method, surpassing state-of-the-art algorithms in both conventional image quality evaluation metrics and fMRI downstream tasks. This work in this paper paves the way for sharing massive fMRI data at low bandwidth and high fidelity.

Junhwa Hur, Charles Herrmann, Saurabh Saxena, Janne Kontkanen, Wei-Sheng Lai, Yichang Shih, Michael Rubinstein, David J. Fleet, Deqing Sun

Despite the recent progress, existing frame interpolation methods still struggle with processing extremely high resolution input and handling challenging cases such as repetitive textures, thin objects, and large motion. To address these issues, we introduce a patch-based cascaded pixel diffusion model for high resolution frame interpolation, HiFI, that excels in these scenarios while achieving competitive performance on standard benchmarks. Cascades, which generate a series of images from low to high resolution, can help significantly with large or complex motion that require both global context for a coarse solution and detailed context for high resolution output. However, contrary to prior work on cascaded diffusion models which perform diffusion on increasingly large resolutions, we use a single model that always performs diffusion at the same resolution and upsamples by processing patches of the inputs and the prior solution. At inference time, this drastically reduces memory usage and allows a single model, solving both frame interpolation (base model’s task) and spatial up-sampling, saving training cost as well. HiFI excels at high-resolution images and complex repeated textures that require global context, achieving comparable or state-of-the-art performance on various benchmarks (Vimeo, Xiph, X-Test, and SEPE-8K). We further introduce a new dataset, LaMoR, that focuses on particularly challenging cases, and HiFI significantly outperforms other baselines.

Feng Shibo, Peilin Zhao, Liu Liu, Pengcheng Wu, Zhiqi Shen

Generative models have gained significant attention in multivariate time series forecasting (MTS), particularly due to their ability to generate high-fidelity samples. Forecasting the probability distribution of multivariate time series is a challenging yet practical task. Although some recent attempts have been made to handle this task, two major challenges persist: 1) some existing generative methods underperform in high-dimensional multivariate time series forecasting, which is hard to scale to higher dimensions; 2) The inherent high-dimensional multivariate attributes constrain the forecasting lengths of existing generative models. In this paper, we point out that discrete token representations can model high-dimensional MTS with faster inference time, and forecast the target with the long-term trends of itself can extend the forecasting length with high accuracy. Motivated by this, we propose a vector quantized framework called Hierarchical Discrete Transformer (HDT) that models time series into discrete token representations with l2 normalization enhanced vector quantized strategy, in which we transform the MTS forecasting into discrete tokens generation. To address the limitations of generative models in long-term forecasting, we propose a hierarchical discrete Transformer. This model captures the discrete long-term trend of the target at the low level and leverages this trend as a condition to generate the discrete representation of the target at the high level that introduces the features of target itself for extending the forecasting length in high-dimensional MTS. Extensive experiments on five popular MTS datasets verify the effectiveness of our proposed method. The source code will be released.

Jie Chen, Liangmin Wang, Huijuan Zhu, Victor S. Sheng

Reentrancy vulnerabilities in smart contracts have been exploited to steal enormous amounts of money, thus detecting reentrancy vulnerabilities is a hotspot issue in security research. However, a new attack is emerging in which attackers continuously release new reentrancy patterns to exploit fresh vulnerabilities and obfuscate existing ones. Existing detection methods neglect the time-series evolution of vulnerabilities across different smart contract versions, leading to a gradual decline in their effectiveness over time. We investigate the time-series correlations among vulnerabilities in various versions and refer to these as Evolutionary Reentrancy Vulnerabilities (ERVs). We summarize that ERVs detection faces two key challenges: (i) capturing the evolving pattern of ERVs along a complete evolutionary chain and (ii) detecting fresh reentrancy vulnerabilities in new versions. To address these challenges, we propose CLEP, a novel Contrastive Learning with Evolving Pairs detection method. It can effectively capture the evolving patterns by discerning similarities and differences across versions. Specifically, we first modified the sample distribution by incorporating version declarations as time-series evolution information. Then, leveraging the hierarchical similarity, we design an evolving pairs scheme to form negative and positive contract pairs across versions. Finally, we build a complete evolutionary chain by proposing a version-aware contrastive sampler. Our experimental results show that CLEP not only outperforms state-of-the-art baselines in version-specific scenarios but also shows promising performance in cross-version evolution scenarios.