In today's fast-paced world, advertisers are increasingly demanding real-time and accurate personalized ad delivery based on dynamic preference modeling, which emphasizes the temporality existing in both user preference and product characteristics. Meanwhile, with the development of graph neural networks (GNNs), E-commerce knowledge graphs (KG) with rich semantic relatedness are invoked to improve accuracy and provide appropriate explanations to encourage advertisers' willingness to invest in ad expenses. However, it is still challenging for existing methods to comprehensively consider both time-series interactions and graph-structured knowledge triples in a unified model, i.e., the case in knowledge-aware dynamic advertising. The interaction graph between users and products changes rapidly over time, while the knowledge in KG remains relatively stable. This results in an uneven distribution of temporal and semantic information, causing existing GNNs to fail in this scenario. In this work, we quantitatively define the above phenomenon as temporal unevenness and introduce the Incremental Leveling Network (InLN) with three novel techniques: the periodic-focusing window for node-level dynamic modeling, the biased temporal walk for subgraph-level dynamic modeling and the incremental leveling mechanism for KG updating. Verified by comprehensive and intensive experiments, InLN outperforms nine baseline models in three tasks by substantial margins, reaching up to a 9.9% improvement and averaging a 5.7% increase.
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Multivariate time series classification (MTSC) has attracted significant research attention due to its diverse real-world applications. Recently, exploiting transformers for MTSC has achieved state-of-the-art performance. However, existing methods focus on generic features, providing a comprehensive understanding of data, but they ignore class-specific features crucial for learning the representative characteristics of each class. This leads to poor performance in the case of imbalanced datasets or datasets with similar overall patterns but differing in minor class-specific details. In this paper, we propose a novel Shapelet Transformer (ShapeFormer), which comprises class-specific and generic transformer modules to capture both of these features. In the class-specific module, we introduce the discovery method to extract the discriminative subsequences of each class (i.e. shapelets) from the training set. We then propose a Shapelet Filter to learn the difference features between these shapelets and the input time series. We found that the difference feature for each shapelet contains important class-specific features, as it shows a significant distinction between its class and others. In the generic module, convolution filters are used to extract generic features that contain information to distinguish among all classes. For each module, we employ the transformer encoder to capture the correlation between their features. As a result, the combination of two transformer modules allows our model to exploit the power of both types of features, thereby enhancing the classification performance. Our experiments on 30 UEA MTSC datasets demonstrate that ShapeFormer has achieved the highest accuracy ranking compared to state-of-the-art methods. The code is available at https://github.com/xuanmay2701/shapeformer.
ReCTSi: Resource-efficient Correlated Time Series Imputation via Decoupled Pattern Learning and Completeness-aware Attentions
PDF ↗Imputation of Correlated Time Series (CTS) is essential in data preprocessing for many tasks, particularly when sensor data is often incomplete. Deep learning has enabled sophisticated models that improve CTS imputation by capturing temporal and spatial patterns. However, deep models often incur considerable consumption of computational resources and thus cannot be deployed in resource-limited settings. This paper presents ReCTSi (Resource-efficient CTS imputation), a method that adopts a new architecture for decoupled pattern learning in two phases: (1) the Persistent Pattern Extraction phase utilizes a multi-view learnable codebook mechanism to identify and archive persistent patterns common across different time series, enabling rapid pattern retrieval during inference. (2) the Transient Pattern Adaptation phase introduces completeness-aware attention modules that allocate attention to the complete and hence more reliable data segments. Extensive experimental results show that ReCTSi achieves state-of-the-art imputation accuracy while consuming much fewer computational resources than the leading existing model, consuming only 0.004% of the FLOPs for inference compared to its closest competitor. The blend of high accuracy and very low resource consumption makes ReCTSi the currently best method for resource-limited scenarios. The related code is available at https://github.com/ryanlaics/RECTSI.
In multivariate time series (MTS) classification, finding the important features (e.g., sensors) for model performance is crucial yet challenging due to the complex, high-dimensional nature of MTS data, intricate temporal dynamics, and the necessity for domain-specific interpretations. Current explanation methods for MTS mostly focus on time-centric explanations, apt for pinpointing important time periods but less effective in identifying key features. This limitation underscores the pressing need for a feature-centric approach, a vital yet often overlooked perspective that complements time-centric analysis. To bridge this gap, our study introduces a novel feature-centric explanation and evaluation framework for MTS, named CAFO (Channel Attention and Feature Orthgonalization). CAFO employs a convolution-based approach with channel attention mechanisms, incorporating a depth-wise separable channel attention module (DepCA) and a QR decomposition-based loss for promoting feature-wise orthogonality. We demonstrate that this orthogonalization enhances the separability of attention distributions, thereby refining and stabilizing the ranking of feature importance. This improvement in feature-wise ranking enhances our understanding of feature explainability in MTS. Furthermore, we develop metrics to evaluate global and class-specific feature importance. Our framework's efficacy is validated through extensive empirical analyses on two major public benchmarks and real-world datasets, both synthetic and self-collected, specifically designed to highlight class-wise discriminative features. The results confirm CAFO's robustness and informative capacity in assessing feature importance in MTS classification tasks. This study not only advances the understanding of feature-centric explanations in MTS but also sets a foundation for future explorations in feature-centric explanations. The codes are available at https://github.com/eai-lab/CAFO.
Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization
PDF ↗Time series forecasting has been an essential field in many different application areas, including economic analysis, meteorology, and so forth. The majority of time series forecasting models are trained using the mean squared error (MSE). However, this training based on MSE causes a limitation known as prediction delay. The prediction delay, which implies the ground-truth precedes the prediction, can cause serious problems in a variety of fields, e.g., finance and weather forecasting --- as a matter of fact, predictions succeeding ground-truth observations are not practically meaningful although their MSEs can be low. This paper proposes a new perspective on traditional time series forecasting tasks and introduces a new solution to mitigate the prediction delay. We introduce a continuous-time gated recurrent unit (GRU) based on the neural ordinary differential equation (NODE) which can supervise explicit time-derivatives. We generalize the GRU architecture in a continuous-time manner and minimize the prediction delay through our time-derivative regularization. Our method outperforms in metrics such as MSE, Dynamic Time Warping (DTW) and Time Distortion Index (TDI). In addition, we demonstrate the low prediction delay of our method in a variety of datasets.
Over the past two decades, time series motif discovery has become a crucial subroutine for many time series data mining tasks; concurrently, it has been established that Dynamic Time Warping (DTW) outperforms other similarity measures like Euclidean Distance in most scenarios. Against this backdrop, a DTW motif discovery algorithm was recently developed; however, it is confined to working with fixed-length subsequences. In this work, we propose a novel approach that allows us to find motifs under both length differences and warping. Our algorithm exploits a promising time series representation called Spikelets and introduces the first lower bound for DTW in the Spikelet space. Extensive empirical studies demonstrate that our method scales effectively across various real-world datasets and efficiently identifies DTW motif pairs of different lengths.
Evaluating anomaly detection algorithms in time series data is critical as inaccuracies can lead to flawed decision-making in various domains where real-time analytics and data-driven strategies are essential. Traditional performance metrics assume iid data and fail to capture the complex temporal dynamics and specific characteristics of time series anomalies, such as early and delayed detections. We introduce Proximity-Aware Time series anomaly Evaluation (PATE), a novel evaluation metric that incorporates the temporal relationship between prediction and anomaly intervals. PATE uses proximity-based weighting considering buffer zones around anomaly intervals, enabling a more detailed and informed assessment of a detection. Using these weights, PATE computes a weighted version of the area under the Precision and Recall curve. Our experiments with synthetic and real-world datasets show the superiority of PATE in providing more sensible and accurate evaluations than other evaluation metrics. We also tested several state-of-the-art anomaly detectors across various benchmark datasets using the PATE evaluation scheme. The results show that a common metric like Point-Adjusted F1 Score fails to characterize the detection performances well, and that PATE is able to provide a more fair model comparison. By introducing PATE, we redefine the understanding of model efficacy that steers future studies toward developing more effective and accurate detection models.
Automated machine learning (AutoML) streamlines the creation of ML models, but few specialized methods have approached the challenging domain of time series forecasting. Deep neural networks (DNNs) often deliver state-of-the-art predictive performance for forecasting data, however these models are also criticized for being computationally intensive black boxes. As a result, when searching for the "best" model, it is crucial to also acknowledge other aspects, such as interpretability and resource consumption. In this paper, we propose AutoXPCR - a novel method that produces DNNs for forecasting under consideration of multiple objectives in an automated and explainable fashion. Our approach leverages meta-learning to estimate any model's performance along PCR criteria, which encompass (P)redictive error, (C)omplexity, and (R)esource demand. Explainability is addressed on multiple levels, as AutoXPCR pro-vides by-product explanations of recommendations and allows to interactively control the desired PCR criteria importance and trade-offs. We demonstrate the practical feasibility AutoXPCR across 108 forecasting data sets from various domains. Notably, our method outperforms competing AutoML approaches - on average, it only requires 20% of computation costs for recommending highly efficient models with 85% of the empirical best quality.
Unsupervised anomaly detection in multivariate time series (MTS) has always been a challenging problem, and the modeling based on reconstruction has garnered significant attention. The insensitivity of these methods towards normal patterns poses challenges in distinguishing between normal and abnormal points. Firstly, the general reconstruction strategies may exhibit limited sensitivity to spatio-temporal dependencies, and their performance remains largely unaffected by such dependencies. Secondly, most methods fail to model the heteroscedastic uncertainty in MTS, hindering their abilities to derive a distinguishable criterion. For instance, normal data with high noise levels may lead to detection failure due to excessively high reconstruction errors. In this work, we emphasize the necessity of sensitivity to normal patterns, which could improve the discrimination between normal and abnormal points remarkably. To this end, we propose SensitiveHUE, a probabilistic network by implementing both reconstruction and heteroscedastic uncertainty estimation. Its core includes a statistical feature removal strategy to ensure the dependency sensitive property, and a novel MTS-NLL loss for modeling the normal patterns in important regions. Experimental results demonstrate that SensitiveHUE exhibits nontrivial sensitivity to normal patterns and outperforms the existing state-of-the-art alternatives by a large margin. Code is publicly available at this URL\footnotehttp://github.com/yuesuoqingqiu/SensitiveHUE.
Spatiotemporal time series forecasting plays a key role in a wide range of real-world applications. While significant progress has been made in this area, fully capturing and leveraging spatiotemporal heterogeneity remains a fundamental challenge. Therefore, we propose a novel Heterogeneity-Informed Meta-Parameter Learning scheme. Specifically, our approach implicitly captures spatiotemporal heterogeneity through learning spatial and temporal embeddings, which can be viewed as a clustering process. Then, a novel spatiotemporal meta-parameter learning paradigm is proposed to learn spatiotemporal-specific parameters from meta-parameter pools, which is informed by the captured heterogeneity. Based on these ideas, we develop a Heterogeneity-Informed Spatiotemporal Meta-Network (HimNet) for spatiotemporal time series forecasting. Extensive experiments on five widely-used benchmarks demonstrate our method achieves state-of-the-art performance while exhibiting superior interpretability. Our code is available at https://github.com/XDZhelheim/HimNet.
Disease surveillance, traffic management, and weather forecasting are some of the key applications that could benefit from block maxima forecasting of a time series as the extreme block maxima values often signify events of critical importance such as disease outbreaks, traffic gridlock, and severe weather conditions. As the use of deep neural network models for block maxima forecasting increases, so does the need for explainable AI methods that could unravel the inner workings of such black box models. To fill this need, this paper presents a novel counterfactual explanation framework for block maxima forecasting models. Unlike existing methods, our proposed framework, DiffusionCF, combines deep anomaly detection with a conditional diffusion model to identify unusual patterns in the time series that could help explain the forecasted extreme block maxima. Experimental results on several real-world datasets demonstrate the superiority of DiffusionCF over other baseline methods when evaluated according to various metrics, particularly their informativeness and closeness. Our data and codes are available at https://github.com/yue2023cs/DiffusionCF.
To counter the side effect brought by the proliferation of social media platforms, hate speech detection (HSD) plays a vital role in halting the dissemination of toxic online posts at an early stage. However, given the ubiquitous topical communities on social media, a trained HSD classifier can easily become biased towards specific targeted groups (e.g.,female andblack people), where a high rate of either false positive or false negative results can significantly impair public trust in the fairness of content moderation mechanisms, and eventually harm the diversity of online society. Although existing fairness-aware HSD methods can smooth out some discrepancies across targeted groups, they are mostly specific to a narrow selection of targets that are assumed to be known and fixed. This inevitably prevents those methods from generalizing to real-world use cases where new targeted groups constantly emerge (e.g., new forums created on Reddit) over time. To tackle the defects of existing HSD practices, we propose Generalizable target-aware Fairness (GetFair), a new method for fairly classifying each post that contains diverse and even unseen targets during inference. To remove the HSD classifier's spurious dependence on target-related features, GetFair trains a series of filter functions in an adversarial pipeline, so as to deceive the discriminator that recovers the targeted group from filtered post embeddings. To maintain scalability and generalizability, we innovatively parameterize all filter functions via a hypernetwork. Taking a target's pretrained word embedding as input, the hypernetwork generates the weights used by each target-specific filter on-the-fly without storing dedicated filter parameters. In addition, a novel semantic gap alignment scheme is imposed on the generation process, such that the produced filter function for an unseen target is rectified by its semantic affinity with existing targets used for training. Finally, experiments are conducted on two benchmark HSD datasets, showing advantageous performance of GetFair on out-of-sample targets among baselines.
Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift
PDF ↗Recent years have witnessed the success of introducing deep learning models to time series forecasting. From a data generation perspective, we illustrate that existing models are susceptible to distribution shifts driven by temporal contexts, whether observed or unobserved. Such context-driven distribution shift (CDS) introduces biases in predictions within specific contexts and poses challenges for conventional training paradigms. In this paper, we introduce a universal calibration methodology for the detection and adaptation of CDS with a trained model. To this end, we propose a novel CDS detector, termed the "residual-based CDS detector" or "Reconditionor", which quantifies the model's vulnerability to CDS by evaluating the mutual information between prediction residuals and their corresponding contexts. A high Reconditionor score indicates a severe susceptibility, thereby necessitating model adaptation. In this circumstance, we put forth a straightforward yet potent adapter framework for model calibration, termed the "sample-level contextualized adapter" or "SOLID". This framework involves the curation of a contextually similar dataset to the provided test sample and the subsequent fine-tuning of the model's prediction layer with a limited number of steps. Our theoretical analysis demonstrates that this adaptation strategy can achieve an optimal bias-variance trade-off. Notably, our proposed Reconditionor and SOLID are model-agnostic and readily adaptable to a wide range of models. Extensive experiments show that SOLID consistently enhances the performance of current forecasting models on real-world datasets, especially on cases with substantial CDS detected by the proposed Reconditionor, thus validating the effectiveness of the calibration approach.
Slow task detection is a critical problem in cloud operation and maintenance since it is highly related to user experience and can bring substantial liquidated damages. Most anomaly detection methods detect it from a single-task aspect. However, considering millions of concurrent tasks in large-scale cloud computing clusters, it becomes impractical and inefficient. Moreover, single-task slowdowns are very common and do not necessarily indicate a malfunction of a cluster due to its violent fluctuation nature in a virtual environment. Thus, we shift our attention to cluster-wide task slowdowns by utilizing the duration time distribution of tasks across a cluster, so that the computation complexity is not relevant to the number of tasks. The task duration time distribution often exhibits compound periodicity and local exceptional fluctuations over time. Though transformer-based methods are one of the most powerful methods to capture these time series normal variation patterns, we empirically find and theoretically explain the flaw of the standard attention mechanism in reconstructing subperiods with low amplitude when dealing with compound periodicity. To tackle these challenges, we propose SORN (i.e., Skimming Off subperiods in descending amplitude order and Reconstructing Non-slowing fluctuation), which consists of a Skimming Attention mechanism to reconstruct the compound periodicity and a Neural Optimal Transport module to distinguish cluster-wide slowdowns from other exceptional fluctuations. Furthermore, since anomalies in the training set are inevitable in a practical scenario, we propose a picky loss function, which adaptively assigns higher weights to reliable time slots in the training set. Extensive experiments demonstrate that SORN outperforms state-of-the-art methods on multiple real-world industrial datasets.
Operating and maintaining hyperscale data centers involving millions of service hosts has been an extremely intricate task to tackle for top Internet companies. Incessant system failures cost operators countless hours of browsing through performance metrics to diagnose the underlying root cause to prevent the recurrence. Although many state-of-the-art (SOTA) methods have used time-series causal discovery to construct causal relationships among anomalous metrics, they only focus on homogeneous service-level performance metrics and fail to yield useful insights on heterogeneous host-level metrics. To address the challenge, this study presents FaultInsight, a highly interpretable deep causal host fault diagnosing framework that offers diagnostic insights from various perspectives to reduce human effort in troubleshooting. We evaluate FaultInsight using dozens of incidents collected from our production environment. FaultInsight provides markedly better root cause identification accuracy than SOTA baselines in our incident dataset. It also shows outstanding advantages in terms of deployability in real production systems. Our engineers are deeply impressed by FaultInsight's ability to interpret incidents from multiple perspectives, helping them quickly understand the mechanism behind the faults.
Although time-series classification has many applications in healthcare and manufacturing, the high cost of data collection and labeling hinders its widespread use. To reduce data collection and labeling costs while maintaining high classification accuracy, we propose a novel problem setting, called semi-supervised learning with low-sampling-rate time series, in which the majority of time series are collected at a low sampling rate and are unlabeled whereas the minority of time series are collected at a high sampling rate and are labeled. For this novel problem scenario, we develop the SemiTSR framework equipped with the super-resolution module and the semi-supervised learning module. Here, low-sampling-rate time series are upsampled precisely, taking periodicity and trend at each timestamp into account, and both labeled and unlabeled high-sampling-rate time series are utilized for training. In particular, consistency regularization between artificially downsampled time series derived from an original high-sampling-rate time series is effective at overcoming limited sampling rates. We demonstrate that SemiTSR significantly outperforms conventional semi-supervised learning techniques by assuring high classification accuracy with low-sampling-rate time series.
We present AESim, a data-driven Aircraft Engine Simulator developed using transformer-based conditional generative adversarial networks. AESim generates samples of aircraft engine sensor measurements over full flights, conditioned on a given flight mission profile representing the flight conditions. It constitutes an essential tool in aircraft engine digital twins, capable of simulating their performance for different flight missions. It allows for comparison of the behavior of different engines under the same operational conditions, simulation of various scenarios for a given engine, facilitating applications like engine behavior analysis, performance limit identification, and optimization of maintenance schedules within a global Prognostics and Health Management (PHM) strategy. It also allows the imputation of missing flight data and addresses confidentiality concerns by generating synthetic flight datasets that can be shared for public research purposes or data challenges.
Today industrial facilities are equipped with lots of sensors throughout all the production line for monitoring means. Gathered data can be used to detect and predict failures; however, manual labeling of large amounts of data for supervised learning is complicated. This paper introduces an innovative approach to unsupervised fault detection and diagnosis tailored for monitoring industrial chemical processes. We showcase the efficacy of our model using two publicly accessible datasets from the Tennessee Eastman Process, each containing various faults. Furthermore, we illustrate that by fine-tuning the model on a limited amount of labeled data, it achieves performance close to that of a state-of-the-art model trained on the entire dataset.
Large Language Models (LLMs) have seen significant use in domains such as natural language processing and computer vision. Going beyond text, image and graphics, LLMs present a significant potential for analysis of time series data, benefiting domains such as climate, IoT, healthcare, traffic, audio and finance. This survey paper provides an in-depth exploration and a detailed taxonomy of the various methodologies employed to harness the power of LLMs for time series analysis. We address the inherent challenge of bridging the gap between LLMs' original text data training and the numerical nature of time series data, and explore strategies for transferring and distilling knowledge from LLMs to numerical time series analysis. We detail various methodologies, including (1) direct prompting of LLMs, (2) time series quantization, (3) aligning techniques, (4) utilization of the vision modality as a bridging mechanism, and (5) the combination of LLMs with tools. Additionally, this survey offers a comprehensive overview of the existing multimodal time series and text datasets in diverse domains, and discusses the challenges and future opportunities of this emerging field.
Recently, remarkable progress has been made over large language models (LLMs), demonstrating their unprecedented capability in varieties of natural language tasks. However, completely training a large general-purpose model from the scratch is challenging for time series analysis, due to the large volumes and varieties of time series data, as well as the non-stationarity that leads to concept drift impeding continuous model adaptation and re-training. Recent advances have shown that pre-trained LLMs can be exploited to capture complex dependencies in time series data and facilitate various applications. In this survey, we provide a systematic overview of existing methods that leverage LLMs for time series analysis. Specifically, we first state the challenges and motivations of applying language models in the context of time series as well as brief preliminaries of LLMs. Next, we summarize the general pipeline for LLM-based time series analysis, categorize existing methods into different groups (\textit{i.e.}, direct query, tokenization, prompt design, fine-tune, and model integration), and highlight the key ideas within each group. We also discuss the applications of LLMs for both general and spatial-temporal time series data, tailored to specific domains. Finally, we thoroughly discuss future research opportunities to empower time series analysis with LLMs.