In the field of material science, the analysis of the properties of crystalline materials is of key importance. Recently, machine learning has become a prominent tool for predicting the properties of materials based on their structure. However, the application of machine learning to crystal property prediction faces two significant challenges. The first is the scarcity of labeled data, due to the time-consuming and resource-intensive process of crystal property labeling. The second is the importance of leveraging specialized knowledge when performing crystal structure analysis, which requires adapting machine learning methods specifically for the crystal domain. In this paper, we propose a new semi-supervised framework, aKnowledge-BasedSemi-Supervised crystal property prediction (KBSS) framework, which employs consistency regularization to leverage both labeled and unlabeled data while incorporating crystal knowledge guidance. Specifically, to use unlabeled data efficiently, the KBSS framework incorporates two key modules: a knowledge-guided augmentation (KGA) module and an adaptive pseudo-label filtering (APF) module. The KGA module utilizes the Monte Carlo method to leverage knowledge from the crystal domain to guide weak and strong augmentations of crystal structures. The APF module enhances the pseudo-labeling process for unlabeled crystal data by enabling task-guided uncertainty adjustment and category-aware pseudo-label selection. The experimental results show that KBSS achieves state-of-the-art performance. All code is publicly available athttps://github.com/HaominYu0/KBSS.

The deployment of Internet of Things and cyber-physical technologies leads to increased collection of spatiotemporal data. In the transportation domain, this data is being used in an expanding range of applications, e.g., to enhance road safety. The capture of changes to traffic signs, e.g., due to wear, road construction, accidents, or theft, helps ensure well-functioning and safe road networks. However, this process has so far been largely manual, making it costly and meaning that changes are often captured with considerable delays. To capture changes more cost-effectively and more frequently, we introduce AutoTracker, an automated reporting system that exploits low-cost cameras in vehicles to capture changes. The system features a three-layer architecture—perception, analysis, and storage—that incorporates vehicle motion dynamics for improved performance. The analysis layer integrates physical modeling, R-tree-based retrieval, and calibration components to refine detection results. We also introduce new datasets and evaluation metrics to assess the performance of AutoTracker. Experiments show that AutoTracker is capable of state-of-the-art accuracy and efficiency in traffic sign change reporting.

Time series classification (TSC) is an important task in time series analysis. Existing TSC methods mainly train on each single domain separately, suffering from a degradation in accuracy when the samples for training are insufficient in certain domains. The pre-training and fine-tuning paradigm provides a promising direction for solving this problem. However, time series from different domains are substantially divergent, which challenges the effective pre-training on multi-source data and the generalization ability of pre-trained models. To handle this issue, we introduce Augmented Series and Image Contrastive Learning for Time Series Classification (AimTS), a pre-training framework that learns generalizable representations from multi-source time series data. We propose a two-level prototype-based contrastive learning method to effectively utilize various augmentations in multi-source pre-training, which learns representations for TSC that can be generalized to different domains. In addition, considering augmentations within the single time series modality are insufficient to fully address classification problems with distribution shift, we introduce the image modality to supplement structural information and establish a series-image contrastive learning to improve the generalization of the learned representations for TSC tasks. Extensive experiments show that after multi-source pre-training, AimTS achieves good generalization performance, enabling efficient learning and even few-shot learning on various downstream TSC datasets.

We study the problem of time series anomaly prediction, which is relevant to a range of real-world applications. Existing anomaly prediction methods rely on labeled training data for achieving acceptable accuracy. However, such data may be difficult to obtain; and in real-time deployments, anomalies can occur that were not seen in labeled data, thus making them difficult to predict. We provide a theoretical analysis and propose an Importance-based Generative Contrastive Learning method (IGCL) for unsupervised anomaly prediction. IGCL employs a controlled diffusion module to produce anomaly precursor patterns. Next, ICGL learns contextual representations to extract temporal dependencies from pairs of normal time series and anomaly precursors. IGCL is then able to predict anomalies by identifying anomaly precursors that will evolve into future anomalies. To address challenges caused by potentially complex precursor combinations involving multiple variables, we propose a memory bank with importance scores that stores representative samples adaptively and generates more complex anomaly precursors. Extensive experiments on nine benchmark datasets offer evidence that the proposed method is able to outperform state-of-the-art baselines.

Accurate time series forecasting is crucial for optimizing resource allocation, industrial production, and urban management, particularly with the growth of cyber-physical and IoT systems. However, limited training sample availability in fields like physics and biology poses significant challenges. Existing models struggle to capture long-term dependencies and to model diverse meta-knowledge explicitly in few-shot scenarios. To address these issues, we propose MetaGP, a meta-learning-based Gaussian process latent variable model that uses a Gaussian process kernel function to capture long-term dependencies and to maintain strong correlations in time series. We also introduce Kernel Association Search (KAS) as a novel meta-learning component to explicitly model meta-knowledge, thereby enhancing both interpretability and prediction accuracy. We study MetaGP on simulated and real-world few-shot datasets, showing that it is capable of state-of-the-art prediction accuracy. We also find that MetaGP can capture long-term dependencies and can model meta-knowledge, thereby providing valuable insights into complex time series patterns.

Transformers for time series forecasting mainly model time series from limited or fixed scales, making it challenging to capture different characteristics spanning various scales. We propose Pathformer, a multi-scale Transformer with adaptive pathways. It integrates both temporal resolution and temporal distance for multi-scale modeling. Multi-scale division divides the time series into different temporal resolutions using patches of various sizes. Based on the division of each scale, dual attention is performed over these patches to capture global correlations and local details as temporal dependencies. We further enrich the multi-scale Transformer with adaptive pathways, which adaptively adjust the multi-scale modeling process based on the varying temporal dynamics of the input, improving the accuracy and generalization of Pathformer. Extensive experiments on eleven real-world datasets demonstrate that Pathformer not only achieves state-of-the-art performance by surpassing all current models but also exhibits stronger generalization abilities under various transfer scenarios. The code is made available at https://github.com/decisionintelligence/pathformer.

Accurate long-term forecasting from multivariate time series has important real-world applications. However, achieving this so is challenging. Thus, analyses reveal that time series that span long durations often exhibit dynamic and disrupted correlations. State-of-the-art methods employ attention mechanisms to capture dynamic correlations, but they often do not contend well with disrupted correlations, which reduces prediction accuracy. We introduce local and global information concepts and then leverage these in a Memory Guided Transformer, called the Memformer. By integrating patch-wise recurrent graph learning and global attention, the Memformer aims to capture dynamic correlations and take disrupted correlations into account. We also integrate a so-called Alternating Memory Enhancer into the Memformer to capture correlations between local and global information. We report on experiments that offer insight into the effectiveness of the Memformer at capturing dynamic correlations and its robustness to disrupted correlations. The experiments offer evidence that the new method is capable of advancing the state-of-the-art in forecasting accuracy on real-world datasets.

Robust multivariate time series forecasting is crucial in many cyberphysical and Internet of Things applications. Existing state-of-the-art robust forecasting models decompose time series into independent functions covering trends and periodicities. However, these independent functions fail to capture correlations among multiple time series, thereby reducing prediction accuracy. Moreover, existing robust forecasting models treat certain abrupt but normal changes, e.g., caused by holidays, as outliers because they occur infrequently and have data distributions that resemble those of outliers. This exacerbates model bias and reduces prediction accuracy. This paper aims to capture correlations across multiple time series and abrupt but normal changes, thereby improving prediction accuracy. We employ weak labels to partition the dataset into source and target domains. Then, we propose the Domain Adversarial Robust Forecaster (DARF). This forecasting model is based on adversarial domain adaptation and includes two novel modules: Correlated Robust Forecaster (CORF) and Domain Critic. Specifically, CORF constitutes an encoder-decoder framework proficient at robust multivariate time series forecasting, and Domain Critic works to reduce data bias. Extensive experiments and discussions show that DARF is capable of state-of-the-art forecasting accuracy.

Predictive autoscaling is a key enabler for optimizing cloud resource allocation in Alibaba Cloud's computing platforms, which dynamically adjust the Elastic Compute Service (ECS) instances based on predicted user demands to ensure Quality of Service (QoS). However, user demands in the cloud are often highly complex, with high uncertainty and scale-sensitive temporal dependencies, thus posing great challenges for accurate prediction of future demands. These in turn make autoscaling challenging---autoscaling needs to properly account for demand uncertainty while maintaining a reasonable trade-off between two contradictory factors, i.e., low instance running costs vs. low QoS violation risks. To address the above challenges, we propose a novel predictive autoscaling framework MagicScaler , consisting of a Multi-scale attentive Gaussian process based predictor and an uncertainty-aware scaler. First, the predictor carefully bridges the best of two successful prediction methodologies---multi-scale attention mechanisms, which are good at capturing complex, multi-scale features, and stochastic process regression, which can quantify prediction uncertainty, thus achieving accurate demand prediction with quantified uncertainty. Second, the scaler takes the quantified future demand uncertainty into a judiciously designed loss function with stochastic constraints, enabling flexible trade-off between running costs and QoS violation risks. Extensive experiments on three clusters of Alibaba Cloud in different Chinese cities demonstrate the effectiveness and efficiency of MagicScaler , which outperforms other commonly adopted scalers, thus justifying our design choices.

Multiple time series forecasting plays an essential role in many applications. Solutions based on graph neural network (GNN) that deliver state-of-the-art forecasting performance use the relation graph which can capture historical correlations among time series. However, in real world, it is common that correlations among time series evolve across time, resulting in dynamic relation graph, where the future correlations may be different from those in history. To address this problem, we propose multiple time series forecasting with dynamic graph modeling (MTSF-DG) that is able to learn historical relation graphs and predicting future relation graphs to capture the dynamic correlations. We also propose a causal GNN to extract features from both kinds of relation graphs efficiently. Then we propose a reasoning network to explicitly learn the variant influence from historical timestamps to future timestamps for final forecasting. Extensive experiments on six benchmark datasets show that MTSF-DG consistently outperforms state-of-the-art baselines, and justify our design with dynamic relation graph modeling.