PA-RNet: Perturbation-Aware Residual Network for Robust Multimodal Time Series Forecasting
Institute of Computing Technology, Guangzhou University（广州大学计算机技术学院）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）
Enqiang Zhu, Zhenbin Deng, Shengzhi Wang, Yi-Kun Tang, Chanjuan Liu
In real-world applications, multimodal time-series forecasting faces a key challenge: textual information is often useful but unreliable. Auxiliary texts may contain irrelevant, ambiguous, incomplete, or structurally corrupted content, making direct text integration prone to introducing noisy semantic signals and degrading forecasting performance. Therefore, robust multimodal forecasting requires a model that can exploit useful textual context while suppressing misleading perturbations. To address this challenge, we propose PA-RNet, a carefully designed perturbation-aware residual network for robust multimodal time-series forecasting. Rather than directly fusing textual and numerical representations, PA-RNet first refines multimodal features in a perturbation-aware manner, preserving stable contextual information while reducing unstable or misleading signals. The refined textual representations are then aligned with temporal dynamics, enabling more reliable forecasting under noisy multimodal conditions. Theoretically, we prove that PA-RNet is Lipschitz continuous with respect to textual embeddings and show that the proposed spectral residual correction can reduce the expected prediction error under zero-mean textual perturbations. We further conduct supplementary experiments with injected textual perturbations to examine the robustness of PA-RNet. The results across diverse domains demonstrate that PA-RNet consistently outperforms state-of-the-art baselines and maintains stable forecasting performance under both original and noise-perturbed textual conditions.
Predicting traffic conditions is tremendously challenging since every road is highly dependent on each other, both spatially and temporally. Recently, to capture this spatial and temporal dependency, specially designed architectures such as graph convolutional networks and temporal convolutional networks have been introduced. While there has been remarkable progress in traffic forecasting, we found that deep-learning-based traffic forecasting models still fail in certain patterns, mainly in event situations (e.g., rapid speed drops). Although it is commonly accepted that these failures are due…
TEFL: Prediction-Residual-Guided Rolling Forecasting for Multi-Horizon Time Series
Key Laboratory of Road and Traffic Engineering, Ministry of Education Tongji University, Shanghai China（交通工程教育部重点实验室，同济大学，上海中国）；The College of Computer Science, Tongji University, Shanghai China（计算机科学学院，同济大学，上海中国）；Research Center for Scientific Data Hub, Zhejiang Lab, Hangzhou, China（科学数据中心研究中心，浙江实验室，杭州中国）
CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition
School of Artificial Intelligence and Data Science, University of Science and Technology of China（中国科学技术大学人工智能与数据科学学院）；Alibaba Group（阿里巴巴集团）；Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）人工智能学域）
ReLaMix: Residual Latency-Aware Mixing for Delay-Robust Financial Time-Series Forecasting
Lanzhou University（兰州大学）；Brandeis University（品牌斯大学）；Shenzhen University（深圳大学）；Central University of Finance and Economics（中央财经大学）；Central China Normal University（中央师范大学）
DSPR: Dual-Stream Physics-Residual Networks for Trustworthy Industrial Time Series Forecasting
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Research Center, East Hope Group Co., Ltd（东希望集团有限公司研究院）
Due to the increasing availability of large-scale observation and simulation datasets, data-driven representations arise as efficient and relevant computation representations of dynamical systems for a wide range of applications, where model-driven models based on ordinary differential equation remain the state-of-the-art approaches. In this work, we investigate neural networks (NN) as physically-sound data-driven representations of such systems. Reinterpreting Runge-Kutta methods as graphical models, we consider a residual NN architecture and introduce bilinear layers to embed non-linearities…
Crowd flow forecasting, which aims to predict the crowds entering or leaving certain regions, is a fundamental task in smart cities. One of the key properties of crowd flow data is periodicity: a pattern that occurs at regular time intervals, such as a weekly pattern. To capture such periodicity, existing studies either fuse the periodic hidden states into channels for networks to learn or apply extra periodic strategies to the network architecture. In this paper, we devise a novel periodic residual learning network (PRNet) for a better modeling of periodicity in crowd flow data. Unlike existi…
University of Minnesota, Twin Cities（明尼苏达大学双城分校）；IBM Research, T. J. Watson Research Center（IBM研究院托马斯·J·沃森研究中心）
Multivariate time-series modeling and forecasting is an important problem with numerous applications. Traditional approaches such as VAR (vector auto-regressive) models and more recent approaches such as RNNs (recurrent neural networks) are indispensable tools in modeling time-series data. In many multivariate time series modeling problems, there is usually a significant linear dependency component, for which VARs are suitable, and a nonlinear component, for which RNNs are suitable. Modeling such times series with only VAR or only RNNs can lead to poor predictive performance or complex models …
Time series forecasting is widely used in the fields of equipment life cycle forecasting, weather forecasting, traffic flow forecasting, and other fields. Recently, some scholars have tried to apply Transformer to time series forecasting because of its powerful parallel training ability. However, the existing Transformer methods do not pay enough attention to the small time segments that play a decisive role in prediction, making it insensitive to small changes that affect the trend of time series, and it is difficult to effectively learn continuous time-dependent features. To solve this probl…
Czestochowa University of Technology（琴斯托霍瓦理工大学）；Uber Technologies（优步科技公司）
This work presents a hybrid and hierarchical deep learning model for mid-term load forecasting. The model combines exponential smoothing (ETS), advanced Long Short-Term Memory (LSTM) and ensembling. ETS extracts dynamically the main components of each individual time series and enables the model to learn their representation. Multi-layer LSTM is equipped with dilated recurrent skip connections and a spatial shortcut path from lower layers to allow the model to better capture long-term seasonal relationships and ensure more efficient training. A common learning procedure for LSTM and ETS, with …
ReCycle: Fast and Efficient Long Time Series Forecasting with Residual Cyclic Transformers
Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；German Aerospace Center (DLR)（德国航空航天中心）；INENSUS GmbH（因内森斯有限公司）；Siemens AG, Digital Industries（西门子股份公司数字工业部门）
StateFlow: Dual-State Recurrent Modeling for Long-Horizon Time Series Forecasting
Department of Computer Science, Illinois Institute of Technology, Chicago, IL, USA（伊利诺伊理工学院计算机科学系）；Department of Computer Science, Emory University, Atlanta, GA, USA（埃默里大学计算机科学系）
Frequency-Aware Attention-LSTM for PM$_{2.5}$ Time Series Forecasting
Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；College of Computer Science and AI Southwest Minzu University（西南民族大学计算机科学与人工智能学院）；School of Information Technology Guangxi Police College（广西警察学院信息技术学院）
Częstochowa University of Technology（琴斯托霍瓦工业大学）；Facebook（脸书）
Time series forecasting is a challenging problem particularly when a time series expresses multiple seasonality, nonlinear trend and varying variance. In this work, to forecast complex time series, we propose ensemble learning which is based on randomized neural networks, and boosted in three ways. These comprise ensemble learning based on residuals, corrected targets and opposed response. The latter two methods are employed to ensure similar forecasting tasks are solved by all ensemble members, which justifies the use of exactly the same base models at all stages of ensembling. Unification of…
Mobile crowdsourcing has become easier thanks to the widespread of smartphones capable of seamlessly collecting and pushing the desired data to cloud services. However, the success of mobile crowdsourcing relies on balancing the supply and demand by first accurately forecasting spatially and temporally the supply-demand gap, and then providing efficient incentives to encourage participant movements to maintain the desired balance. In this paper, we propose Deep-Gap, a deep learning approach based on residual learning to predict the gap between mobile crowdsourced service supply and demand at a…
CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns
School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；Pengcheng Laboratory（鹏城实验室）；The Chinese University of Hong Kong（香港中文大学）；College of Information Science and Technology, Jinan University（暨南大学信息科学技术学院）
本文提出 CycleNet，通过残差周期预测（RCF）技术显式建模时间序列周期性并结合 Linear 或浅层 MLP 进行预测，在多领域实现最优精度且大幅减少参数，作为即插即用技术还能提升现有模型性能。
CARE: A Lightweight Plug-in Gated Correction and Uncertainty-aware Module for Long-term Time Series Forecasting
Institute of Artificial Intelligence, University of Science and Technology Beijing（北京科技大学人工智能研究院）；Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education, University of Science and Technology Beijing（北京科技大学智能仿生无人系统重点实验室）；School of Artificial Intelligence, University of Science and Technology Beijing（北京科技大学人工智能学院）
Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond
The University of Hong Kong（香港大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；The Simons Institute for the Theory of Computing（西蒙斯理论计算研究所）；UC Berkeley（加州大学伯克利分校）；Sun Yat-sen University（中山大学）
Selective Learning for Deep Time Series Forecasting
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所人工智能安全国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Department of Automation, Tsinghua University（清华大学自动化系）
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Zhejiang University（浙江大学）；University of Sydney（悉尼大学）；Northwestern University（西北大学）；Analogy AI, Inc.（Analogy AI公司）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Bitcoin, one of the major cryptocurrencies, presents great opportunities and challenges with its tremendous potential returns accompanying high risks. The high volatility of Bitcoin and the complex factors affecting them make the study of effective price forecasting methods of great practical importance to financial investors and researchers worldwide. In this paper, we propose a novel approach called MRC-LSTM, which combines a Multi-scale Residual Convolutional neural network (MRC) and a Long Short-Term Memory (LSTM) to implement Bitcoin closing price prediction. Specifically, the Multi-scale…
We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties, being interpretable, applicable without modification to a wide array of target domains, and fast to train. We test the proposed architecture on several well-known datasets, including M3, M4 and TOURISM competition datasets containing time series from diverse domains. We demonstrate state-of-the-art performance for tw…
