论文检索

Eamonn J. Keogh

Repeated structures in time series (time series motifs) are interesting in their own right and are often used in downstream algorithms for tasks as diverse as classification, clustering, rule-discovery, segmentation, summarization, compression and anomaly detection. If a structure is repeated, that hints at some mechanism of conservation, and the discovery of conserved structure is one of the most basic tools/goals of science. In this survey (which is a companion to a tutorial) I show the two key ideas needed to do successful time series motif discovery. First, making a computationally hard problem tractable; by the use of anytime algorithms, contract algorithms and specialist hardware. I will further review work on how to obtain more meaningful results by considering additional constraints on the returned patterns. For example, class conditional motifs (i.e. shapelets), motifs with a drift (i.e. time series chains), motifs that exist in two or more time series (motif-joins, consensus motifs), range motifs, KNN motifs etc. The companion tutorial is illustrated with novel interesting examples from science, industry, entertainment and medicine. Moreover, the tutorial slides will contain code snippets and a data archive that will allow the community to reproduce all the results and then generalize them to their own domain of interest.

Yushan Jiang, Kanghui Ning, Zijie Pan, Xuyang Shen, Jingchao Ni, Wenchao Yu, Anderson Schneider, Haifeng Chen, Yuriy Nevmyvaka, Dongjin Song

Multi-modal time series analysis has recently emerged as a prominent research area, driven by the increasing availability of diverse data modalities, such as text, images, and structured tabular data from real-world sources. However, effective analysis of multi-modal time series is hindered by data heterogeneity, modality gap, misalignment, and inherent noise. Recent advancements in multi-modal time series methods have exploited the multi-modal context via cross-modal interactions based on deep learning methods, significantly enhancing various downstream tasks. In this tutorial and survey, we present a systematic and up-to-date overview of multi-modal time series datasets and methods. We first state the existing challenges of multi-modal time series analysis and our motivations, with a brief introduction of preliminaries. Then, we summarize the general pipeline and categorize existing methods through a unified cross-modal interaction framework encompassing fusion, alignment, and transference at different levels (i.e., input, intermediate, output), where key concepts and ideas are highlighted. We also discuss the real-world applications of multi-modal analysis for both standard and spatial time series, tailored to general and specific domains. Finally, we discuss future research directions to help practitioners explore and exploit multi-modal time series. The up-to-date resources are provided in the GitHub repository. https://github.com/UConn-DSIS/Multi-modal-Time-Series-Analysis.

Ruifeng Tan, Weixiang Hong 0002, Jiayue Tang, Xibin Lu, Ruijun Ma, Xiang Zheng, Jia Li 0009, Jiaqiang Huang, Tong-Yi Zhang

Battery Life Prediction (BLP), which relies on time series data produced by battery degradation tests, is crucial for battery utilization, optimization, and production. Despite impressive advancements, this research area faces three key challenges. Firstly, the limited size of existing datasets impedes insights into modern battery life data. Secondly, most datasets are restricted to small-capacity lithium-ion batteries tested under a narrow range of diversity in labs, raising concerns about the generalizability of findings. Thirdly, inconsistent and limited benchmarks across studies obscure the effectiveness of baselines and leave it unclear if models popular in other time series fields are effective for BLP. To address these challenges, we propose BatteryLife, a comprehensive dataset and benchmark for BLP. BatteryLife integrates 16 datasets, offering a 2.5 times sample size compared to the previous largest dataset, and provides the most diverse battery life resource with batteries from 8 formats, 59 chemical systems, 9 operating temperatures, and 421 charge/discharge protocols, including both laboratory and industrial tests. Notably, BatteryLife is the first to release battery life datasets of zinc-ion batteries, sodium-ion batteries, and industry-tested large-capacity lithium-ion batteries. With the comprehensive dataset, we revisit the effectiveness of baselines popular in this and other time series fields. Furthermore, we propose CyclePatch, a plug-in technique that can be employed in various neural networks. Extensive benchmarking of 18 methods reveals that models popular in other time series fields can be unsuitable for BLP, and CyclePatch consistently improves model performance establishing state-of-the-art benchmarks. Moreover, BatteryLife evaluates model performance across aging conditions and domains. BatteryLife is available at https://github.com/Ruifeng-Tan/BatteryLife.

Zhe Li 0011, Xiangfei Qiu, Peng Chen 0038, Yihang Wang 0004, Hanyin Cheng, Yang Shu 0001, Jilin Hu, Chenjuan Guo, Aoying Zhou, Christian S. Jensen 等

Time Series Forecasting (TSF) is key functionality in numerous fields, such as financial investment, weather services, and energy management. Although increasingly capable TSF methods occur, many of them require domain-specific data collection and model training and do not generalize well when applied in other domains. Time Series Foundation Models (TSFMs) that are pre-trained on massive heterogeneous time series data aim to overcome these limitations. The prospects for generalizability have spurred the development of a new generation of TSFMs. This study proposes a benchmark, TSFM-Bench, to facilitate comprehensive and unified evaluation of TSFMs. TSFM-Bench covers a wide range of TSFMs, including those based on large language models and those pre-trained on time series data. TSFM-Bench supports multiple forecasting scenarios, including zero-shot, few-shot, and full-shot, enabling assessment across the full range of adaptation strategies. TSFM-Bench also provides a standardized experimental protocols for critical evaluation processes such as dataset splitting, loading, normalization, and few-shot sampling, facilitating consistency and fairness. We report on an extensive evaluation of TSFMs across a diverse range of datasets spanning multiple domains and exhibiting varied statistical characteristics. Specifically, we identify pros and cons and inherent limitations of existing TSFMs, and we propose potential directions for new model designs.

Muhammad Hasan Ferdous, Emam Hossain, Md. Osman Gani

Robust causal discovery in time series datasets depends on reliable benchmark datasets with known ground-truth causal relationships. However, such datasets remain scarce, and existing synthetic alternatives often overlook critical temporal properties inherent in real-world data, including nonstationarity driven by trends and seasonality, irregular sampling intervals, and the presence of unobserved confounders. To address these challenges, we introduce TimeGraph, a comprehensive suite of synthetic time-series benchmark datasets that systematically incorporates both linear and nonlinear dependencies while modeling key temporal characteristics such as trends, seasonal effects, and heterogeneous noise patterns. Each dataset is accompanied by a fully specified causal graph featuring varying densities and diverse noise distributions and is provided in two versions: one including unobserved confounders and one without, thereby offering extensive coverage of real-world complexity while preserving methodological neutrality. We further demonstrate the utility of TimeGraph through systematic evaluations of state-of-the-art causal discovery algorithms including PCMCI+, LPCMCI, and FGES across a diverse array of configurations and metrics. Our experiments reveal significant variations in algorithmic performance under realistic temporal conditions, underscoring the need for robust synthetic benchmarks in the fair and transparent assessment of causal discovery methods. The complete TimeGraph suite, including dataset generation scripts, evaluation metrics, and recommended experimental protocols, is freely available to facilitate reproducible research and foster community-driven advancements in time-series causal discovery.

Shiyu Wang 0001, Wei Lu 0030, Jiawei Li 0017, Xiaoming Shi 0001, Xinyue Zhong, Zhou Ye 0001, Ming Jin 0005, Qingsong Wen

Many real-world applications contain data in the form of multivariate time series (TS) with the hierarchical structure, where classic methods forecasting each TS independently are inadequate for coherency (i.e., satisfying the hierarchical aggregation constraints). Furthermore, the discrepancies between statistical properties of different levels can be huge, exacerbated by non-Gaussian distributions and non-linear correlations. In this paper, we propose a novel end-to-end hierarchical TS forecasting model, i.e., a Flow-based Reconcile Transformer (FRT). FRT employs a conditional normalizing flow-based autoregressive transformer, to represent complex data distribution, while simultaneously reconciling the forecasts to ensure coherency. Go beyond other state-of-the-art methods, FRT accomplishes forecasting and reconciliation simultaneously, while avoiding any post-processing steps. Moreover, FRT is a deep model that does not rely on any strong assumptions such as unbiased estimates or Gaussian distribution. Our experiments are conducted on four real-world hierarchical datasets from different industrial domains (three public ones and a dataset from the application servers of our company's data center) and the results demonstrate the efficacy of our proposed method. Our method has been implemented extensively within the production environments of a prominent global payment company. It has emerged as a cornerstone for workload forecasting within their data center and plays a critical role in the optimization of cloud computing resource allocation across the entire cluster. This successful deployment of the application demonstrates that our approach achieves precise hierarchical workload prediction, which is of great significance for efficient resource scheduling, enhancing resource utilization, and reducing server resource use and energy consumption in data centers.

Shihao Tu, Yang Yang 0009, Wenyue Ding, Yicheng Lu, Qingkai Ren, Yupeng Zhang, Yin Zhang 0006

The chemical industry is faced with the urgent challenge of effectively harnessing the vast amounts of time-series data generated by thousands of sensors, which is essential for forecasting chemical states, achieving accurate real-time control of production processes. Traditional forecasting methods suffer from high computational latency and struggle with the complexity of spatiotemporal dependencies. As a result, modeling this data becomes challenging. This paper introduces a novel approach, referred to as ASTNet, designed to address these challenges. ASTNet integrates an asynchronous spatiotemporal modeling framework that combines temporal and spatial encoders, enabling concurrent learning of temporal and spatial dependencies while reducing computational latency. Additionally, it introduces a gated graph fusion mechanism that adaptively combines static (meta) and evolving (dynamic) sensor graphs, enhancing the handling of heterogeneous sensor data and spatial correlations. Extensive experiments on three real-world chemical sensor datasets demonstrate that ASTNet outperforms SOTA methods in terms of both prediction accuracy and computational efficiency, making ASTNet successfully deployed in chemical engineering industrial scenarios.

Shwetha Salimath, Francesca Bugiotti, Sylvain Wlodarczyk

In geoscience, it is necessary to study the lithography of the Earth's subsurface, which consists of different stratified layers called geological formations. This study performs well correlation task to model and characterize reservoirs. This operation links the beginning of specific geological formations called tops using measurements from drilled wells. Although data are abundant, the traditional algorithms used for well correlation are semi-automated, requiring significant time and high computational cost. This paper introduces GeoTS, a Python library to apply cutting-edge time series classification models to perform well correlation in a completely automated setting. As input, take the drilling trajectory depth and gamma-ray well logs, which measure the natural radioactivity across the well depth trajectory. The top depths of the formations are predicted as an output. The gamma-ray signatures are extracted around the top depths assigned by geologists. Preprocessing is performed to clean and cluster these signatures using the Dynamic Time Wrapping (DTW) distance and HDBSCAN. Implementation of existing deep learning architectures (FCN, InceptionTime, XceptionTime, XCM, LSTM-FCN) and new architecture (LSTM-2dCNN, LSTM-XCM) are performed. Our experiments demonstrate faster computation with an increase in accuracy. GradCAM has also been implemented for model explainability. Experiments were performed using Colorado oil fields and deployed on Wyoming oil fields. The deployment has provided us with critical insights regarding the improvements needed.

Fuyuan Lyu, Linfeng Du, Yunpeng Weng, Qiufang Ying, Zhiyan Xu, Wen Zou, Haolun Wu, Xiuqiang He 0001, Xing Tang 0007

Fund allocation has been an increasingly important problem in the financial domain. In reality, we aim to allocate the funds to buy certain assets within a certain future period. Naive solutions such as prediction-only or Predict-then-Optimize approaches suffer from goal mismatch. Additionally, the introduction of the SOTA time series forecasting model inevitably introduces additional uncertainty in the predicted result. To solve both problems mentioned above, we introduce a Risk-aware Time-Series Predict-and-Allocate (RTS-PnO) framework, which holds no prior assumption on the forecasting models. Such a framework contains three features: (i) end-to-end training with objective alignment measurement(ii) adaptive forecasting uncertainty calibration, and (iii) agnostic towards forecasting models. The evaluation of RTS-PnO is conducted over both online and offline experiments. For offline experiments, eight datasets from three categories of financial applications are used: Currency, Stock, and Cryptos. RTS-PnO consistently outperforms other competitive baselines. The online experiment is conducted on the Cross-Border Payment business at FiT, Tencent, and an 8.4% decrease in regret is witnessed when compared with the product-line approach. The code for the offline experiment is available.

Jun Liu, Chaoyun Zhang, Jiaxu Qian, Minghua Ma, Si Qin, Chetan Bansal, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang 0001

Time series anomaly detection (TSAD) plays a crucial role in various industrial applications. Traditional deep learning TSAD models require extensive training data and operate as black boxes, lacking interpretability for detected anomalies. To address these challenges, we propose LLMAD, a novel TSAD method that employs Large Language Models (LLMs) to deliver accurate and interpretable TSAD results. LLMAD applies in-context anomaly detection by retrieving both positive and negative similar time series segments, significantly enhancing LLMs' effectiveness. Furthermore, LLMAD employs the Anomaly Detection Chain-of-Thought approach to mimic expert logic for its decision-making process. This further enhances its performance and enables LLMAD to provide explanations for their detections through versatile perspectives. Experiments conducted on both offline datasets and through online deployment on a large-scale cloud system at Microsoft indicate that our LLMAD achieves detection performance comparable to state-of-the-art deep learning methods. Additionally, LLMAD offers human-readable interpretability for detections at a reasonable cost, significantly reducing engineers' effort.

Jiahao Ji, Tianyu Wang 0028, Yeshu Li, Yusen Huo, Zhilin Zhang 0003, Chuan Yu 0002, Jian Xu 0015, Bo Zheng 0007

Auto-bidding is crucial in facilitating online advertising by automatically providing bids for advertisers. While previous work has made great efforts to model bidding environments for better ad performance, it has limitations in generalizability across environments since these models are typically tailored for specific bidding scenarios. To this end, we approach the scenario-independent principles through a unified function that estimates the achieved effect under specific bids, such as budget consumption, gross merchandise volume (GMV), page views, etc. Then, we propose a bidding foundation model Bid2X to learn this fundamental function from data in various scenarios. Our Bid2X is built over uniform series embeddings that encode heterogeneous data through tailored embedding methods. To capture complex inter-variable and dynamic temporal dependencies in bidding data, we propose two attention mechanisms separately treating embeddings of different variables and embeddings at different times as attention tokens for representation learning. On top of the learned variable and temporal representations, a variable-aware fusion module is used to perform adaptive bidding outcome prediction. To model the unique bidding data distribution, we devise a zero-inflated projection module to incorporate the estimated non-zero probability into its value prediction, which makes up a joint optimization objective containing classification and regression. The objective is proven to converge to the zero-inflated distribution. Our model has been deployed on the ad platform in Taobao, one of the world's largest e-commerce platforms. Offline evaluation on eight large-scale real-world datasets exhibits Bid2X's superiority compared to various baselines and its generality across different scenarios. In real-world applications, Bid2X increased GMV by 4.65% and ROI by 2.44% in online A/B tests, paving the way for the bidding foundation model in computational advertising.

Qiming Chen, Yingying Zhang, Qingsong Wen, Liang Sun 0001

Accurate periodicity detection of performance metrics in cloud platform is essential for enhancing monitoring accuracy and ensure service quality of cloud computing. However, in real-world industrial settings, such as large-scale cloud computing platform MaxCompute at Alibaba Cloud, performance metrics often exhibit complex multi-periodicity and temporal nonstationarity. Additionally, these metrics are frequently contaminated by noise and anomalies, rendering traditional periodicity detection methods ineffective. To address these challenges, this paper proposes a robust and general period detection method with low deployment cost and high usability. The method employs Empirical Mode Decomposition (EMD) to decompose complex time series into Intrinsic Mode Functions (IMFs), isolating noise, periodic, and trend components. Significant periods are identified using statistical tests to eliminate irrelevant components, while an automatic clustering mechanism mitigates the mode-mixing problem inherent in EMD. Extensive experiments on synthetic and real-world datasets demonstrate that the proposed method outperforms state-of-the-art periodicity detection techniques, achieving over 10% and 17% performance improvements in public single and multiple periodicity detection tasks, respectively. Deployed in Alibaba Cloud's MaxCompute platform, it monitors millions of tasks, resulting in a 7.8% increase in fault detection coverage and enabling faults to be detected 13.15% more rapidly. The method's low deployment cost and user-friendly implementation make it highly accessible and practical for diverse cloud monitoring applications.

Pengfei Zhou, Yunlong Liu, Junli Liang, Qi Song 0004, Xiangyang Li 0001

Time series forecasting with exogenous variables is a critical emerging paradigm that presents unique challenges in modeling dependencies between variables. Traditional models often struggle to differentiate between endogenous and exogenous variables, leading to inefficiencies and overfitting. In this paper, we introduce CrossLinear, a novel Linear-based forecasting model that addresses these challenges by incorporating a plug-and-play cross-correlation embedding module. This lightweight module captures the dependencies between variables with minimal computational cost and seamlessly integrates into existing neural networks. Specifically, it captures time-invariant and direct variable dependencies while disregarding time-varying or indirect dependencies, thereby mitigating the risk of overfitting in dependency modeling and contributing to consistent performance improvements. Furthermore, CrossLinear employs patch-wise processing and a global linear head to effectively capture both short-term and long-term temporal dependencies, further improving its forecasting precision. Extensive experiments on 12 real-world datasets demonstrate that CrossLinear achieves superior performance in both short-term and long-term forecasting tasks. The ablation study underscores the effectiveness of the cross-correlation embedding module. Additionally, the generalizability of this module makes it a valuable plug-in for various forecasting tasks across different domains. Codes are available at https://github.com/mumiao2000/CrossLinear.

Shuhan Zhong, Weipeng Zhuo, Sizhe Song, Guanyao Li, Zhongyi Yu, S.-H. Gary Chan

Irregular multivariate time series (IMTS) is characterized by the lack of synchronized observations across its different channels. In this paper, we point out that this channel-wise asynchrony can lead to poor channel-wise modeling of existing deep learning methods. To overcome this limitation, we propose MTM, a multi-scale token mixing transformer for the classification of IMTS. We find that the channel-wise asynchrony can be alleviated by down-sampling the time series to coarser timescales, and propose to incorporate a masked concat pooling in MTM that gradually down-samples IMTS to enhance the channel-wise attention modules. Meanwhile, we propose a novel channel-wise token mixing mechanism which proactively chooses important tokens from one channel and mixes them with other channels, to further boost the channel-wise learning of our model. Through extensive experiments on real-world datasets and comparison with state-of-the-art methods, we demonstrate that MTM consistently achieves the best performance on all the benchmarks, with improvements of up to 3.8% in AUPRC for classification.

Liangwei Nathan Zheng, Chang George Dong, Wei Emma Zhang, Lin Yue, Miao Xu 0001, Olaf Maennel, Weitong Chen 0001

Large Language Models (LLMs) have demonstrated impressive performance in time series analysis and seems to understand the time temporal relationship well than traditional transformer-based approaches. However, since LLMs are not designed for time series tasks, simpler models-like linear regressions can often achieve comparable performance with far less complexity. In this study, we perform extensive experiments to assess the effectiveness of applying LLMs to key time series tasks, including forecasting, classification, imputation, and anomaly detection. We compare the performance of LLMs against simpler baseline models, such as single-layer linear models and randomly initialized LLMs. Our results reveal that LLMs offer minimal advantages for these core time series tasks and may even distort the temporal structure of the data. In contrast, simpler models consistently outperform LLMs while requiring far fewer parameters. Furthermore, we analyze existing reprogramming techniques and show, through data manifold analysis, that these methods fail to effectively align time series data with language and display ''pseudo-alignment'' behavior in embedding space. Our findings suggest that the performance of LLM-based methods in time series tasks arises from the intrinsic characteristics and structure of time series data, rather than any meaningful alignment with the language model architecture. We release the code for experiments here: https://github.com/IcurasLW/Official-Repository_Understanding_LLM_for_Time_Series_Analysis.git

Zhiyuan Zhao 0002, Haoxin Liu 0001, Alexander Rodríguez, B. Aditya Prakash

Time-series forecasting is a critical challenge in various domains and has witnessed substantial progress in recent years. Many real-life scenarios, such as public health, economics, and social applications, involve feedback loops where predictive models can trigger actions that influence the outcome they aim to predict, subsequently altering the target variable's distribution. This phenomenon, known as performativity, introduces the potential for 'self-negating' or 'self-fulfilling' predictions. Despite extensive studies on performativity in classification problems across domains, this phenomenon remains largely unexplored in the context of time-series forecasting from a machine-learning perspective. In this paper, we formalize Performative Time-Series Forecasting (PeTS), addressing the challenge for predictions when performativity-induced distribution shifts are possible. We propose a novel solution to PeTS, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts and subsequently predicts targets. We provide theoretical insights suggesting that FPS potentially leads to reduced generalization error. Extensive experiment results demonstrate that FPS consistently outperforms conventional time-series forecasting and concept drift methods, highlighting its efficacy in handling performativity-induced challenges.

Kai Zhao 0009, Zhihao Zhuang, Chenjuan Guo, Hao Miao 0001, Christian S. Jensen, Yunyao Cheng 0001, Bin Yang 0002

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.

Yang Zhang 0058, Wenbo Yang, Jun Wang 0089, Qiang Ma 0001, Jie Xiong 0008

该来源暂未提供摘要。

Weijia Zhang 0003, Chenlong Yin, Hao Liu 0026, Hui Xiong 0001

Pre-trained Language Models (PLMs), such as ChatGPT, have significantly advanced the field of natural language processing. This progress has inspired a series of innovative studies that explore the adaptation of PLMs to time series analysis, intending to create a unified foundation model that addresses various time series analytical tasks. However, these efforts predominantly focus on Regularly Sampled Time Series (RSTS), neglecting the unique challenges posed by Irregularly Sampled Time Series (ISTS), which are characterized by uneven sampling intervals and prevalent missing data. To bridge this gap, this work takes the first step in exploring the potential of PLMs for ISTS analysis. We begin by investigating the effect of various methods for representing ISTS, aiming to maximize the efficacy of PLMs in the analysis. Furthermore, we propose a unified PLM-based framework, named ISTS-PLM, to address diverse ISTS analytical tasks. It integrates novel time-aware and variable-aware PLMs tailored to tackle the intractable intra- and inter-time series modeling in ISTS. Finally, extensive experiments on a comprehensive benchmark demonstrate that the ISTS-PLM, utilizing a structured and effective series-based representation for ISTS, consistently achieves state-of-the-art performance across various analytical tasks, such as classification, interpolation, extrapolation, few-shot and zero-shot learning scenarios, spanning scientific domains like healthcare, biomechanics, and climate science.

Shuo Zhang 0015, Jing Wang 0060, Shiqin Nie, Jinghang Yue, Weikang Zhu, Youfang Lin

Incomplete time series classification is both practically valuable and challenging as missing values in time series data are prevalent in real-world scenarios. Current approaches suffer from two major limitations. First, they overemphasize the consistency of data reconstruction during missing value imputation while neglecting the task-effectiveness of the imputed results for the classification. Second, they fail to systematically establish a synergistic optimization mechanism between data imputation and feature representation. To address these challenges, we propose a Hierarchical Conditional Information Bottleneck (HCIB) framework, which achieves incomplete time series classification through end-to-end joint optimization. Specifically, at the data imputation level, we re-examine the dual effects of missing data: the loss of critical information (Loss) versus the gain in interference suppression (Gain), elucidating this duality through bias-variance trade-off theory. Building on this analysis, we propose a task-information sufficiency criterion and extend the information bottleneck theory into a task-driven imputation framework by incorporating label information as a conditional constraint. At the feature representation level, we construct a hierarchical information bottleneck architecture to learn compressed yet informative temporal representations from the task-oriented imputed data. Furthermore, we derive the optimizable objective function for HCIB and design specialized neural network architectures for time series. Comprehensive experiments on multivariate and univariate time series datasets across multiple domains consistently demonstrate that the proposed method achieves significant improvements in classification performance compared to SOTA approaches.