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Haichen Wang, Liu Yang, Xinyuan Zhang, Haomin Yu, Ming Li, Jilin Hu

Passenger demand forecasting helps optimize vehicle scheduling, thereby improving urban efficiency. Recently, attention-based methods have been used to adequately capture the dynamic nature of spatio-temporal data. However, existing methods that rely on heuristic masking strategies cannot fully adapt to the complex spatio-temporal correlations, hindering the model from focusing on the right context. These works also overlook the high-level correlations that exist in the real world. Effectively integrating these high-level correlations with the original correlations is crucial. To fill this gap, we propose the Aggregation Differential Transformer (ADFormer), which offers new insights to demand forecasting promotion. Specifically, we utilize Differential Attention to capture the original spatial correlations and achieve attention denoising. Meanwhile, we design distinct aggregation strategies based on the nature of space and time. Then, the original correlations are unified with the high-level correlations, enabling the model to capture holistic spatio-temporal relations. Experiments conducted on taxi and bike datasets confirm the effectiveness and efficiency of our model, demonstrating its practical value. The code is available at https://github.com/decisionintelligence/ADFormer.

Bin Wang, Yongqi Han, Minbo Ma, Tianrui Li, Junbo Zhang, Feng Hong, Yanwei Yu

Deep learning-based approaches have demonstrated significant advancements in time series forecasting. Despite these ongoing developments, the complex dynamics of time series make it challenging to establish the rule of thumb for designing the golden model architecture. In this study, we argue that refining existing advanced models through a universal calibrating strategy can deliver substantial benefits with minimal resource costs, as opposed to elaborating and training a new model from scratch. We first identify a multi-target learning conflict in the calibrating process, which arises when optimizing variables across time steps, leading to the underutilization of the model's learning capabilities. To address this issue, we propose an innovative calibrating strategy called Socket+Plug (SoP). This approach retains an exclusive optimizer and early-stopping monitor for each predicted target within each Plug while keeping the fully trained Socket backbone frozen. The model-agnostic nature of SoP allows it to directly calibrate the performance of any trained deep forecasting models, regardless of their specific architectures. Extensive experiments on various time series benchmarks and a spatio-temporal meteorological ERA5 dataset demonstrate the effectiveness of SoP, achieving up to a 22% improvement even when employing a simple MLP as the Plug (highlighted in Figure 1).

Meng Wan, Rongqiang Cao, Yanghao Li, Jue Wang, Zijian Wang, Qi Su, Lei Qiu, Peng Shi, Yangang Wang, Chong Li

Deep-learning-based lossless compression is of immense importance in real-world applications, such as cold data persistence, sensor data collection, and astronomical data transmission. However, existing compressors typically model data using single-byte symbols as tokens, which makes it hard to capture the inherent correlations and cannot effectively utilize the parallel capabilities of GPU and multi-core CPU. This paper proposes SEP, a novel lossless compression framework for most time-series backbone neural networks. We first introduce a semantic enhancement module to capture the complex intra-patch relationships of binary byte streams. To improve the compression speed, we design multi-stream pipelines that dynamically assign parallel tasks to GPU streams and multi-cores. We further propose a novel GPU memory optimization strategy, which reuses GPU memory by a shared pool across streams. We conduct experiments on seven real-world datasets and the results demonstrate that our SEP framework outperforms state-of-the-art compressors with an average speed improvement of 30.0% and an average compression ratio gain of 5.1%, which is further elevated to 7.6% with the use of pre-training models. The GPU memory footprint is reduced by as high as 63.1% and by an average of 36.2%. The source code is available at: https://github.com/damonwan1/SEP.

Jiangfeng Sun, Kaiwen Xue, Qika Lin, Yufei Qiao, Yifan Zhu, Zhonghong Ou, Meina Song

Emotion recognition from EEG signals is crucial for understanding complex brain dynamics. Existing methods typically rely on static frequency bands and graph convolutional networks (GCNs) to model brain connectivity. However, EEG signals are inherently non-stationary and exhibit substantial individual variability, making static-band approaches inadequate for capturing their dynamic properties. Moreover, spatial-temporal dependencies in EEG often lead to feature degradation during node aggregation, ultimately limiting recognition performance. To address these challenges, we propose the Spatial-Temporal Electroencephalograph Collaboration framework (Stella). Our approach introduces an Adaptive Bands Selection module (ABS) that dynamically extracts low- and high-frequency components, generating dual-path features comprising phase brain networks for connectivity modeling and time-series representations for local dynamics. To further mitigate feature degradation, the Fourier Graph Operator (FGO) operates in the spectral domain, while the Spatial-Temporal Encoder (STE) enhances representation stability and density. Extensive experiments on benchmark EEG datasets demonstrate that Stella achieves state-of-the-art performance in emotion recognition, offering valuable insights for graph-based modeling of non-stationary neural signals. The code is available at https://github.com/sun2017bupt/EEGBrainNetwork.

Xinyu Su, Feng Liu, Yanchuan Chang, Egemen Tanin, Majid Sarvi, Jianzhong Qi

Traffic forecasting is crucial for transportation systems optimisation. Current models minimise the mean forecasting errors, often favouring periodic events prevalent in the training data, while overlooking critical aperiodic ones like traffic incidents. To address this, we propose DualCast, a dual-branch framework that disentangles traffic signals into intrinsic spatial-temporal patterns and external environmental contexts, including aperiodic events. DualCast also employs a cross-time attention mechanism to capture high-order spatial-temporal relationships from both periodic and aperiodic patterns. DualCast is versatile. We integrate it with recent traffic forecasting models, consistently reducing their forecasting errors by up to 9.6% on multiple real datasets.

Zhi Sheng, Daisy Yuan, Jingtao Ding, Qi Yan, Xi Zheng, Yue Sun, Yong Li

Accurate prediction of mobile traffic,i.e., network traffic from cellular base stations, is crucial for optimizing network performance and supporting urban development. However, the non-stationary nature of mobile traffic, driven by human activity and environmental changes, leads to both regular patterns and abrupt variations. Diffusion models excel in capturing such complex temporal dynamics due to their ability to capture the inherent uncertainties. Most existing approaches prioritize designing novel denoising networks but often neglect the critical role of noise itself, potentially leading to sub-optimal performance. In this paper, we introduce a novel perspective by emphasizing the role of noise in the denoising process. Our analysis reveals that noise fundamentally shapes mobile traffic predictions, exhibiting distinct and consistent patterns. We propose NPDiff, a framework that decomposes noise into prior and residual components, with the prior derived from data dynamics, enhancing the model's ability to capture both regular and abrupt variations. NPDiff can seamlessly integrate with various diffusion-based prediction models, delivering predictions that are effective, efficient, and robust. Extensive experiments demonstrate that it achieves superior performance with an improvement over 30%, offering a new perspective on leveraging diffusion models in this domain. We provide code and data at https://github.com/tsinghua-fib-lab/NPDiff.

Xiaoye Miao, Han Shi, Yi Yuan, Daozhan Pan, Yangyang Wu, Xiaohua Pan

Multivariate time series (MTS) data in real-world scenarios are often incomplete, which hinders effective data analysis. Therefore, MTS imputation has been widely studied to facilitate various MTS tasks. Existing imputation methods primarily initialize missing values with zeros in order to perform effective incomplete MTS encoding, which impede the model's capacity to precisely discern the missing distribution. Moreover, these methods often overlook the global similarity in time series but are limited in the use of local information within the sample. To this end, we propose a novel multivariate time series imputation network model, named MMNet. MMNet introduces a Missing-Aware Embedding (MAE) approach to adaptively represent incomplete MTS, allowing the model to better distinguish between missing and observed data. Furthermore, we design a Memory-Enhanced Encoder (MEE) aimed at modeling prior knowledge through memory mechanism, enabling better utilization of the global similarity within the time series. Building upon this, MMNet incorporates a Multi-scale Mixing architecture (MSM) that leverages information from multiple scales to enhance the final imputation. Extensive experiments on four public real-world datasets demonstrate that, MMNet yields a more than 25% gain in performance, compared with the state-of-the-art methods.

Jiaming Ma, Zhiqing Cui, Binwu Wang, Pengkun Wang, Zhengyang Zhou, Zhe Zhao, Yang Wang

Air quality prediction plays a crucial role in the development of smart cities, garnering significant attention from both academia and industry. Current air quality prediction models encounter two major limitations: their high computational complexity limits scalability to nationwide datasets, and they often regard weather covariates as optional auxiliary information. In reality, weather covariates can have a substantial impact on air quality indices (AQI), exhibiting a significant causal association. In this paper, we first present a nationwide air quality dataset to address the lack of open-source, large-scale datasets in this field. Then we propose a causal learning model, CauAir, for air quality prediction that harnesses the powerful representation capabilities of the Transformer to explicitly model the causal association between weather covariates and AQI. To address the high complexity of traditional Transformers, we design CachLormer, which features two key innovations: a simplified architecture with redundant components removed, and a cache-attention mechanism that employs learnable embeddings for perceiving causal association between AQI and weather covariates in a coarsegrained perspective. We use information theory to illustrate the superiority of the proposed model. Finally, experimental results on three datasets with 28 as the baseline demonstrate that our model achieves competitive performance, while maintaining high training efficiency and low memory consumption. The source code is available at CauAir Official Repository.

Fan Lu, Xiaolong Xu, Haolong Xiang, Lianyong Qi, Xiaokang Zhou, Fei Dai, Wanchun Dou

Sequential recommendation generates embedding representations from historical user-item interactions to recommend the next potential interaction item. Due to the complexity and variability of historical user-item interactions, extracting effective user features is quite challenging. Recent studies have employed sequential networks such as time series networks and Transformers to capture the intricate dependencies and temporal patterns in historical user-item interactions, extracting more effective user features. However, limited by the scarcity and suboptimal quality of data, these methods struggle to capture subtle differences in user sequences, which results in diminished recommendation accuracy. To address the above issue, we propose a contrastive learning framework with LLMs-based view augmentation (CLLMRec), which effectively mines differences in behavioral sequences through sample generation. Specifically, CLLMRec utilizes LLMs (Large Language Models) to augment views and expand user behavior sequence representations, providing high-quality positive and negative samples. Subsequently, CLLMRec employs the augmented views for effective contrastive learning, capturing subtle differences in behavioral sequences to suppress interference from irrelevant noise. Experimental results on three public datasets demonstrate that the proposed method outperforms state-of-the-art baseline models, and significantly enhances recommendation performance.

Wenying He, Jieling Huang, Junhua Gu, Ji Zhang, Yude Bai

Missing data in spatiotemporal systems presents a significant challenge for modern applications, ranging from environmental monitoring to urban traffic management. The integrity of spatiotemporal data often deteriorates due to hardware malfunctions and software failures in real-world deployments. Current approaches based on machine learning and deep learning struggle to model the intricate interdependencies between spatial and temporal dimensions effectively and, more importantly, suffer from cumulative errors during the data imputation process, which propagate and amplify through iterations. To address these limitations, we propose CoFILL, a novel Conditional Diffusion Model for spatiotemporal data imputation. CoFILL builds on the inherent advantages of diffusion models to generate high-quality imputations without relying on potentially error-prone prior estimates. It incorporates an innovative dual-stream architecture that processes temporal and frequency domain features in parallel. By fusing these complementary features, CoFILL captures both rapid fluctuations and underlying patterns in the data, which enables more robust imputation. The extensive experiments demonstrate that CoFILL's noise prediction network successfully transforms random noise into meaningful values that align with the true data distribution. The results also show that CoFILL outperforms state-of-the-art methods in terms of imputation accuracy. The source code is publicly available at https://github.com/joyHJL/CoFILL.

Enshuo Yan, Huachuan Wang, Weihao Xia

In multivariate time series classification, although current sequence analysis models have excellent classification capabilities, they show significant shortcomings when dealing with long sequence multivariate data. This paper focuses on optimizing model performance for long-sequence multivariate data by mitigating the impact of extended time series and multiple variables on the model. We propose a principal component analysis (PCA)-based temporal streaming compression and dimensionality reduction algorithm for time series data (temporal streaming batch PCA, TSBPCA), which continuously updates the compact representation of the entire sequence through streaming PCA time estimation with time block updates, enhancing the data representation capability of a range of sequence analysis models.We evaluated this method using various models on five datasets, and the experimental results show that our method demonstrates outstanding performance in both classification accuracy and time efficiency.

Hou-Wan Long, On-In Ho, Qi-Qiao He, Yain-Whar Si

Transfer learning enhances model performance in financial time series by leveraging data from related domains. The selection of appropriate source domains is crucial to avoid negative transfer. We propose using Gramian Angular Field (GAF) transformations to improve time series similarity functions for better domain alignment. Extensive experiments with DNN and LSTM models show that GAF-based similarity functions, specifically Coral (GAF) for DNN and CMD (GAF) for LSTM, significantly reduce prediction errors, demonstrating their effectiveness in complex financial environments.

Cristiano Landi

Mobility data from smartphones, connected cars, and GPS devices are widely used for tasks such as transportation mode classification and suspicious movement detection. Time series research, a closely related field, focuses more on classification methods. Yet, Mobility Data analysis faces unique challenges like geographic transferability and limited public data due to privacy issues. My PhD work focuses on developing reusable, interpretable MD representations. I created Trajectory Interval Forest and later Geolet, a shapelet-based transformation to improve MD classification across geographic regions. Ongoing research explores improving geographic transferability and event-based trajectory clustering.

Zahra Ghorrati

Artificial Intelligence (AI) continues to evolve rapidly, impacting numerous fields, including time series (TS) classification and human activity recognition (HAR). Despite the advancements in deep learning models, such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), these models face several challenges, including the need for extensive labeled datasets, significant computational resources, and lack of interpretability. This research aims to address these limitations by developing an adaptive hierarchical deep neural network framework that integrates fuzzy logic principles and adaptive learning techniques for robust, computationally efficient, and interpretable real-time TS analysis. The reduction in the number of parameters and the efficient learning of hierarchical features mean that less training data is needed to achieve robust performance. The model's ability to generalize from hierarchical representations allows it to make effective use of smaller datasets, which is particularly advantageous in scenarios where data is limited or expensive to obtain.The proposed framework specifically targets HAR applications using data from wearable sensors.

Sayantan Basu, Mohit Kumar, Sivaramakrishnan Kaveri

Time series data, prevalent in fields like medical, e-commerce, finance, etc., is used for forecasting, such as predicting next quarter’s product demand based on past trends. However, some problems necessitate causal models to answer questions like “What the product demand would have been without a specific intervention (e.g., products with slower delivery time suppressed from the search results)?” Such questions require causal models to estimate unobserved counterfactual outcome. In this paper, we propose a novel Graph Causal Forecasting (GCF) model, that predicts the unobserved demand leveraging the relationship of a product with other similar products in the marketplace (spatial aspect), along with change in demand over time for each product (temporal aspect). The core idea is to estimate the counterfactual outcome using a synthetic control unaffected by the treatment. Our approach uses RGCN-dilated CNN based network, which leverages domain knowledge to automatically design a synthetic control during training. Using GCF for our demand forecasting problem, we achieve 75.3% lower MAPE compared to baseline. We use the forecasted values to recommend high demand products, in terms of our business metric (discussed later) which tracks the quality of these recommendations, we achieve a significant jump of 61.2%. Moreover, it adds 67.8% more high demand products to the marketplace, compared to existing model in production. Deployment of GCF in 2023, led to +1399 bps improvement in number of products with a view from customers, and +310 bps improvement in number of products with a sale. We also compare GCF with state of the art forecasting methods on a semi-synthetic data, created by simulating a treatment on open source traffic data METR-LA. We achieve 30% lower MSE against TGCN, a time series forecasting approach and 30% lower MSE against CRN and 25% lower MSE against Google Causal Impact model, both of which are causal forecasting approaches.

Xuwei Tan, Qian Zhao, Yanlan Liu, Xueru Zhang

Drought is one of the most destructive and expensive natural disasters, severely impacting natural resources and risks by depleting water resources and diminishing agricultural yields. Under climate change, accurately predicting drought is critical for mitigating drought-induced risks. However, the intricate interplay among the physical and biological drivers that regulate droughts limits the predictability and understanding of drought, particularly at a subseasonal to seasonal (S2S) time scale. While deep learning has demonstrated the potential to address climate forecasting challenges, its application to drought prediction has received relatively less attention. In this work, we propose a new dataset, DroughtSet, which integrates relevant predictive features and three drought indices from multiple remote sensing and reanalysis datasets across the contiguous United States (CONUS). DroughtSet specifically provides the machine learning community with a new real-world dataset to benchmark drought prediction models and more generally, time-series forecasting methods. Furthermore, we propose a spatial-temporal model SPDrought to predict and interpret S2S droughts. Our model learns from the spatial and temporal information of physical and biological features to predict three types of droughts simultaneously. Multiple strategies are employed to quantify the importance of physical and biological features for drought prediction. Our results provide insights for researchers to better understand the predictability and sensitivity of drought to biological and physical conditions. We aim to contribute to the climate field by proposing a new tool to predict and understand the occurrence of droughts and provide the AI community with a new benchmark to study deep learning applications in climate science.

Ajitesh Srivastava

Individual models of infectious diseases or trajectories coming from different simulations may vary considerably, making it challenging for public communication and supporting policy-making. Therefore, it is common in public health to first create a consensus across multiple models and simulations through ensembling. However, current methods are limited to mean and median ensembles that perform aggregation of scale (cases, hospitalizations, deaths) along the time axis, which often misrepresents the underlying trajectories -- e.g., they underrepresent the peak. Instead, we wish to create an ensemble that represents aggregation simultaneously over both time and scale and thus better preserves the properties of the trajectories. This is particularly useful for public health where time-series have a sequence of meaningful local trends that are ordered, e.g., a surge to an increase to a peak to a decrease. We propose a novel alignment method DTW+SBA, which combines a representation of local trends along with dynamic time warping barycenter averaging. We prove key properties of this method that ensure appropriate alignment based on local trends. We demonstrate on real multi-model outputs that our approach preserves the properties of underlying trajectories. We also show that our alignment leads to a more sensible clustering of epidemic trajectories.

Amir Yaghoubi Shahir, Tilemachos Charalampous, Mahsa Keramati, Fatemeh Movafagh, Uwe Glässer, Hans Wehn

Illegal, Unreported, and Unregulated (IUU) fishing aggravates the global crisis caused by overfishing, threatening the sustainability of marine ecosystems and fisheries worldwide. Distinctive operational characteristics of fishing vessels result in unique footprints on marine environments and socio-economic structures, depending on their fishing method and gear type such as trawlers with non-selective gear that disrupts the seabed, purse seiners using Fish Aggregating Devices (FADs), and longliners notorious for high bycatch rates. As these vessels play an essential role in commercial fishing and the industry, effective monitoring, regulation, and enforcement are critical to mitigate the devastating consequences of overfishing and promote sustainable fishing practices. To this end, this paper introduces a novel multi-stage method for Gear type Identification by Spatiotemporal trajectory Transformation (GIST). This method proposes a data-centric approach that employs domain knowledge to facilitate the deployment of an efficient and accurate analysis of operational patterns of fishing vessels derived from Automatic Identification System (AIS) data. Our method first extracts fishing patterns from vessel trajectories to refine data integrity and isolate only the most relevant activities, thereby ensuring a more accurate result. Next, it encapsulates the distributional insights of fishing activities into fixed-sized "images" as actionable input for a multi-class CNN-based classifier. Utilizing GIST bypasses complicated linear analyses of time series data for rendering lengthy trajectories, advancing an efficient gear type identification with 97% accuracy. To the best of our knowledge, GIST is the first to use a multi-stage method to distinguish three principal gear types widely used globally. Our experiments confirm GIST's practicability and effectiveness, marking a significant advancement towards stricter enforcement of regulations in the fight against IUU fishing.

Yingda Fan, Runlong Yu, Janet R. Barclay, Alison P. Appling, Yiming Sun, Yiqun Xie, Xiaowei Jia

Water temperature can vary substantially even across short distances within the same sub-watershed. Accurate prediction of stream water temperature at fine spatial resolutions (i.e., fine scales, ≤ 1 km) enables precise interventions to maintain water quality and protect aquatic habitats. Although spatiotemporal models have made substantial progress in spatially coarse time series modeling, challenges persist in predicting at fine spatial scales due to the lack of data at that scale. To address the problem of insufficient fine-scale data, we propose a Multi-Scale Graph Learning (MSGL) method. This method employs a multi-task learning framework where coarse-scale graph learning, bolstered by larger datasets, simultaneously enhances fine-scale graph learning. Although existing multi-scale or multi-resolution methods integrate data from different spatial scales, they often overlook the spatial correspondences across graph structures at various scales. To address this, our MSGL introduces an additional learning task, cross-scale interpolation learning, which leverages the hydrological connectedness of stream locations across coarse- and fine-scale graphs to establish cross-scale connections, thereby enhancing overall model performance. Furthermore, we have broken free from the mindset that multi-scale learning is limited to synchronous training by proposing an Asynchronous Multi-Scale Graph Learning method (ASYNC-MSGL). Extensive experiments demonstrate the state-of-the-art performance of our method for anti-sparse downscaling of daily stream temperatures in the Delaware River Basin, USA, highlighting its potential utility for water resources monitoring and management.

Jingwen Wang, Dehui Du, Lili Tian, Yikang Chen, Yida Li, YiYang Li

Deep reinforcement learning (DRL) has gained significant attention in autonomous systems, yet its black-box nature and lack of explainability hinder user trust in safety-critical domains such as autonomous driving. Existing experience replay approaches enhance sample efficiency but often fail to capture the internal causality of training data, leading to a convoluted training process that is difficult for humans to explain. In this work, we introduce Experience Replay with Causal Inference (ERCI), an explainable approach that integrates time series representation and causal inference to offer human-aligned explanations for DRL. Specifically, ERCI 1) introduces a novel multivariate time series representation to extract explainable Time Series Causal Factors (TSCF) from experimental data and 2) leverages internal causality in TSCFs with causal inference as a crucial standard for experience replay in DRL training. We evaluate ERCI using multiple baseline algorithms across diverse environments. Results show that ERCI provides human-aligned explanations and further improves sample efficiency through enhanced explainability. Notably, ERCI outperforms other state-of-the-art approaches by 15% in average performance, highlighting its effectiveness and generalizability.