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Found 122 papers, 65 papers with code
A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment
Currently, existing surveys on LLM safety primarily focus on specific stages of the LLM lifecycle, e. g., deployment phase or fine-tuning phase, lacking a comprehensive understanding of the entire "lifechain" of LLMs.
Automating Personalization: Prompt Optimization for Recommendation Reranking
Modern recommender systems increasingly leverage large language models (LLMs) for reranking to improve personalization.
UniEDU: A Unified Language and Vision Assistant for Education Applications
Education materials for K-12 students often consist of multiple modalities, such as text and images, posing challenges for models to fully understand nuanced information in these materials.
MathAgent: Leveraging a Mixture-of-Math-Agent Framework for Real-World Multimodal Mathematical Error Detection
Mathematical error detection in educational settings presents a significant challenge for Multimodal Large Language Models (MLLMs), requiring a sophisticated understanding of both visual and textual mathematical content along with complex reasoning capabilities.
Aligning Multimodal LLM with Human Preference: A Survey
In this paper, we aim to provide a comprehensive and systematic review of alignment algorithms for MLLMs.
LLM Agents for Education: Advances and Applications
Large Language Model (LLM) agents have demonstrated remarkable capabilities in automating tasks and driving innovation across diverse educational applications.
Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models
Time series analysis is crucial for understanding dynamics of complex systems.
How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlook
We notice that many recent TSA works have formed a new research field, i. e., Multiple Modalities for TSA (MM4TSA).
Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey
Spatio-Temporal (ST) data science, which includes sensing, managing, and mining large-scale data across space and time, is fundamental to understanding complex systems in domains such as urban computing, climate science, and intelligent transportation.
A Survey on Post-training of Large Language Models
The emergence of Large Language Models (LLMs) has fundamentally transformed natural language processing, making them indispensable across domains ranging from conversational systems to scientific exploration.
AgentSafe: Safeguarding Large Language Model-based Multi-agent Systems via Hierarchical Data Management
Large Language Model based multi-agent systems are revolutionizing autonomous communication and collaboration, yet they remain vulnerable to security threats like unauthorized access and data breaches.
RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems
This paper proposes a method capable of both identifying possible root cause types for slow queries and ranking these according to their potential for accelerating slow queries.
Brain Foundation Models: A Survey on Advancements in Neural Signal Processing and Brain Discovery
Brain foundation models (BFMs) have emerged as a transformative paradigm in computational neuroscience, offering a revolutionary framework for processing diverse neural signals across different brain-related tasks.
Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement
Time series data are foundational in finance, healthcare, and energy domains.
LAG: LLM agents for Leaderboard Auto Generation on Demanding
This paper introduces Leaderboard Auto Generation (LAG), a novel and well-organized framework for automatic generation of leaderboards on a given research topic in rapidly evolving fields like Artificial Intelligence (AI).
Stable-SPAM: How to Train in 4-Bit More Stably than 16-Bit Adam
This paper comprehensively evaluates several recently proposed optimizers for 4-bit training, revealing that low-bit precision amplifies sensitivity to learning rates and often causes unstable gradient norms, leading to divergence at higher learning rates.
Corrections Meet Explanations: A Unified Framework for Explainable Grammatical Error Correction
Grammatical Error Correction (GEC) faces a critical challenge concerning explainability, notably when GEC systems are designed for language learners.
From Correctness to Comprehension: AI Agents for Personalized Error Diagnosis in Education
Finally, we propose a multi-agent collaborative framework that combines a Time Series Agent for historical analysis and an MLLM Agent for real-time refinement, enhancing error classification and feedback generation.
Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting
Recent advancements in time series forecasting have explored augmenting models with text or vision modalities to improve accuracy.
Position: Empowering Time Series Reasoning with Multimodal LLMs
Understanding time series data is crucial for multiple real-world applications.
OneForecast: A Universal Framework for Global and Regional Weather Forecasting
Accurate weather forecasts are important for disaster prevention, agricultural planning, and water resource management.
Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment Labels
Detecting anomalies in temporal data has gained significant attention across various real-world applications, aiming to identify unusual events and mitigate potential hazards.
CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries
Our evaluation of 16 models reveals significant disparities, with a stronger performance in Western concepts and weaker results in African and Asian contexts.
LLM-Virus: Evolutionary Jailbreak Attack on Large Language Models
While safety-aligned large language models (LLMs) are increasingly used as the cornerstone for powerful systems such as multi-agent frameworks to solve complex real-world problems, they still suffer from potential adversarial queries, such as jailbreak attacks, which attempt to induce harmful content.
Ask-Before-Detection: Identifying and Mitigating Conformity Bias in LLM-Powered Error Detector for Math Word Problem Solutions
The rise of large language models (LLMs) offers new opportunities for automatic error detection in education, particularly for math word problems (MWPs).
A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges
We categorize the field into three dimensions: benchmarks, methodologies, and challenges.
GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network
Time series subsequence anomaly detection is an important task in a large variety of real-world applications ranging from health monitoring to AIOps, and is challenging due to the following reasons: 1) how to effectively learn complex dynamics and dependencies in time series; 2) diverse and complicated anomalous subsequences as well as the inherent variance and noise of normal patterns; 3) how to determine the proper subsequence length for effective detection, which is a required parameter for many existing algorithms.
NetSafe: Exploring the Topological Safety of Multi-agent Networks
Large language models (LLMs) have empowered nodes within multi-agent networks with intelligence, showing growing applications in both academia and industry.
Abnormality Forecasting: Time Series Anomaly Prediction via Future Context Modeling
It then models a normality correlation of the observation data with the forecasting future context to complement the normality modeling of the observation data in foreseeing possible abnormality in the target window.
FoundTS: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting
We propose a new benchmark, FoundTS, to enable thorough and fair evaluation and comparison of such models.
Toward Physics-guided Time Series Embedding
This theory enables us to bypass the parameterized embedding layer and directly employ physical reconstruction techniques to acquire a data embedding representation.
Task-oriented Time Series Imputation Evaluation via Generalized Representers
Time series analysis is widely used in many fields such as power energy, economics, and transportation, including different tasks such as forecasting, anomaly detection, classification, etc.
ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection
As the field of Multimodal Large Language Models (MLLMs) continues to evolve, their potential to revolutionize artificial intelligence is particularly promising, especially in addressing mathematical reasoning tasks.
Mind Scramble: Unveiling Large Language Model Psychology Via Typoglycemia
Research into the external behaviors and internal mechanisms of large language models (LLMs) has shown promise in addressing complex tasks in the physical world.
Evolving Multi-Scale Normalization for Time Series Forecasting under Distribution Shifts
Complex distribution shifts are the main obstacle to achieving accurate long-term time series forecasting.
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
However, despite the success of large-scale pre-training in language and vision domains, pre-trained time series models remain limited in scale and operate at a high cost, hindering the development of larger capable forecasting models in real-world applications.
AI-Driven Virtual Teacher for Enhanced Educational Efficiency: Leveraging Large Pretrain Models for Autonomous Error Analysis and Correction
Students frequently make mistakes while solving mathematical problems, and traditional error correction methods are both time-consuming and labor-intensive.
Knowledge Tagging with Large Language Model based Multi-Agent System
Knowledge tagging for questions is vital in modern intelligent educational applications, including learning progress diagnosis, practice question recommendations, and course content organization.
Time Series Analysis for Education: Methods, Applications, and Future Directions
The contributions of this paper include a detailed taxonomy of educational data, a synthesis of time series techniques with specific educational applications, and a forward-looking perspective on emerging trends and future research opportunities in educational analysis.
LogParser-LLM: Advancing Efficient Log Parsing with Large Language Models
In evaluations on the LogPub benchmark, involving an average of 3. 6 million logs per dataset across 14 datasets, our LogParser-LLM requires only 272. 5 LLM invocations on average, achieving a 90. 6% F1 score for grouping accuracy and an 81. 1% for parsing accuracy.
MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?
The challenges of perceiving high-resolution images and understanding complex real-world scenarios remain urgent issues to be addressed.
RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation
Leveraging RAGLAB, we conduct a fair comparison of 6 RAG algorithms across 10 benchmarks.
Unlocking the Power of LSTM for Long Term Time Series Forecasting
Traditional recurrent neural network architectures, such as long short-term memory neural networks (LSTM), have historically held a prominent role in time series forecasting (TSF) tasks.
Cluster-Wide Task Slowdown Detection in Cloud System
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.
Intelligent Cross-Organizational Process Mining: A Survey and New Perspectives
Process mining, as a high-level field in data mining, plays a crucial role in enhancing operational efficiency and decision-making across organizations.
Knowledge Tagging System on Math Questions via LLMs with Flexible Demonstration Retriever
Knowledge tagging for questions plays a crucial role in contemporary intelligent educational applications, including learning progress diagnosis, practice question recommendations, and course content organization.
TSI-Bench: Benchmarking Time Series Imputation
Despite the development of numerous deep learning algorithms for time series imputation, the community lacks standardized and comprehensive benchmark platforms to effectively evaluate imputation performance across different settings.
A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges
We then highlight this survey for categorizing the existing literature into key application areas, including linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent-based modeling, and other applications.
Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis
This oversight is due to the untapped potential of textual series data and the absence of a comprehensive, high-quality multimodal dataset.
Beyond LLaVA-HD: Diving into High-Resolution Large Multimodal Models
Seeing clearly with high resolution is a foundation of Large Multimodal Models (LMMs), which has been proven to be vital for visual perception and reasoning.
AutoSurvey: Large Language Models Can Automatically Write Surveys
This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys in rapidly evolving fields like artificial intelligence.
TwinS: Revisiting Non-Stationarity in Multivariate Time Series Forecasting
In this paper, we begin by validating this theory through wavelet analysis and propose the Transformer-based TwinS model, which consists of three modules to address the non-stationary periodic distributions: Wavelet Convolution, Period-Aware Attention, and Channel-Temporal Mixed MLP.
Predicting Parking Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach
In this study, we aim to collectively predict future PA across Singapore with complex factors from various domains.
Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting
State Space Models (SSMs) have emerged as a potent tool in sequence modeling tasks in recent years.
NuwaTS: a Foundation Model Mending Every Incomplete Time Series
In this paper, we present \textbf{NuwaTS}, a novel framework that repurposes Pre-trained Language Models (PLMs) for general time series imputation.
CulturePark: Boosting Cross-cultural Understanding in Large Language Models
Cultural bias is pervasive in many large language models (LLMs), largely due to the deficiency of data representative of different cultures.
Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting
Unlike natural language processing and computer vision, the development of Foundation Models (FMs) for time series forecasting is blocked due to data scarcity.
Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting
Spatiotemporal time series forecasting plays a key role in a wide range of real-world applications.
Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting
In practical scenarios, time series forecasting necessitates not only accuracy but also efficiency.
Ranked #8 on
Time Series Forecasting
on ETTm2 (720) Multivariate
A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
Conditioned models, on the other hand, utilize extra information to enhance performance and are similarly divided for both predictive and generative tasks.
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series
To address this limitation, we propose a novel Domain Adaptation Contrastive learning model for Anomaly Detection in multivariate time series (DACAD), combining UDA with contrastive learning.
Automate Knowledge Concept Tagging on Math Questions with LLMs
Knowledge concept tagging for questions plays a crucial role in contemporary intelligent educational applications, including learning progress diagnosis, practice question recommendations, and course content organization.
Large Language Models for Education: A Survey and Outlook
The advent of Large Language Models (LLMs) has brought in a new era of possibilities in the realm of education.
UrbanVLP: Multi-Granularity Vision-Language Pretraining for Urban Socioeconomic Indicator Prediction
Our UrbanVLP seamlessly integrates multi-granularity information from both macro (satellite) and micro (street-view) levels, overcoming the limitations of prior pretrained models.
Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adapt
For the state-of-the-art (SOTA) model, the MSE is reduced by $33. 3\%$.
Foundation Models for Time Series Analysis: A Tutorial and Survey
Time series analysis stands as a focal point within the data mining community, serving as a cornerstone for extracting valuable insights crucial to a myriad of real-world applications.
Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond
In this paper, we present a comprehensive review of the development and recent advances in deep learning for trajectory computing (DL4Traj).
Online GNN Evaluation Under Test-time Graph Distribution Shifts
This enables the effective evaluation of the well-trained GNNs' ability to capture test node semantics and structural representations, making it an expressive metric for estimating the generalization error in online GNN evaluation.
Developing and Deploying Industry Standards for Artificial Intelligence in Education (AIED): Challenges, Strategies, and Future Directions
Finally, we outline a strategic roadmap for stakeholders to implement these standards, fostering a cohesive and ethical AIED ecosystem.
Debiasing Multimodal Large Language Models
In the realms of computer vision and natural language processing, Large Vision-Language Models (LVLMs) have become indispensable tools, proficient in generating textual descriptions based on visual inputs.
DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting
In this paper, we introduce for the first time the concept of spatio-temporal data dynamic sparse training and are committed to adaptively, dynamically filtering important sensor distributions.
PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations
Recent advancements in deep learning have led to the development of various models for long-term multivariate time-series forecasting (LMTF), many of which have shown promising results.
Generative Semi-supervised Graph Anomaly Detection
This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the extensively explored unsupervised setting with a fully unlabeled graph.
Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective
In long-term time series forecasting (LTSF) tasks, an increasing number of models have acknowledged that discrete time series originate from continuous dynamic systems and have attempted to model their dynamical structures.
Deep Learning for Multivariate Time Series Imputation: A Survey
In this paper, we conduct a comprehensive survey on the recently proposed deep learning imputation methods.
Position: What Can Large Language Models Tell Us about Time Series Analysis
Time series analysis is essential for comprehending the complexities inherent in various realworld systems and applications.
Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting
Multi-scale division divides the time series into different temporal resolutions using patches of various sizes.
Ranked #40 on
Time Series Forecasting
on ETTh1 (336) Multivariate
RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies
Time series forecasting is an important and forefront task in many real-world applications.
Bringing Generative AI to Adaptive Learning in Education
The recent surge in generative AI technologies, such as large language models and diffusion models, has boosted the development of AI applications in various domains, including science, finance, and education.
Explaining Time Series via Contrastive and Locally Sparse Perturbations
Explaining multivariate time series is a compound challenge, as it requires identifying important locations in the time series and matching complex temporal patterns.
HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling for Long-Term Forecasting
Time series forecasting is crucial and challenging in the real world.
Ranked #18 on
Time Series Forecasting
on ETTh1 (336) Multivariate
Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection
Anomaly detection significantly enhances the robustness of cloud systems.
Load Data Valuation in Multi-Energy Systems: An End-to-End Approach
Accurate load forecasting serves as the foundation for the flexible operation of multi-energy systems (MES).
RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models
Large language model (LLM) applications in cloud root cause analysis (RCA) have been actively explored recently.
UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the Web
To answer the questions, we leverage the power of Large Language Models (LLMs) and introduce the first-ever LLM-enhanced framework that integrates the knowledge of textual modality into urban imagery profiling, named LLM-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining (UrbanCLIP).
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
In this survey, we offer a comprehensive and up-to-date review of large models tailored (or adapted) for time series and spatio-temporal data, spanning four key facets: data types, model categories, model scopes, and application areas/tasks.
LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection
Retraining a large neural network model with limited data is vulnerable to overfitting.
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
We begin by reprogramming the input time series with text prototypes before feeding it into the frozen LLM to align the two modalities.
Ranked #1 on
Time Series Forecasting
on ETTh1 (48)
ADGym: Design Choices for Deep Anomaly Detection
Deep learning (DL) techniques have recently found success in anomaly detection (AD) across various fields such as finance, medical services, and cloud computing.
OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data.
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition
More importantly, almost all methods assume the observations are sampled at regular time stamps, and fail to handle complex irregular sampled time series arising from different applications.
WeaverBird: Empowering Financial Decision-Making with Large Language Model, Knowledge Base, and Search Engine
We present WeaverBird, an intelligent dialogue system designed specifically for the finance domain.
EasyTPP: Towards Open Benchmarking Temporal Point Processes
In this paper, we present EasyTPP, the first central repository of research assets (e. g., data, models, evaluation programs, documentations) in the area of event sequence modeling.
Benchmarks and Custom Package for Energy Forecasting
However, there are many differences between energy forecasting and traditional time series forecasting.
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: forecasting, classification, anomaly detection, and imputation.
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
On the other hand, contrastive learning aims to find a representation that can clearly distinguish any instance from the others, which can bring a more natural and promising representation for time series anomaly detection.
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
To fill this gap, we review current state-of-the-art SSL methods for time series data in this article.
SaDI: A Self-adaptive Decomposed Interpretable Framework for Electric Load Forecasting under Extreme Events
In this paper, we propose a novel forecasting framework, named Self-adaptive Decomposed Interpretable framework~(SaDI), which ensembles long-term trend, short-term trend, and period modelings to capture temporal characteristics in different components.
DiffLoad: Uncertainty Quantification in Electrical Load Forecasting with the Diffusion Model
The uncertainties in load forecasting can be divided into two types: epistemic uncertainty and aleatoric uncertainty.
CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting
In this work, we design a special Transformer, i. e., Channel Aligned Robust Blend Transformer (CARD for short), that addresses key shortcomings of CI type Transformer in time series forecasting.
How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?
In this paper, we establish a theoretical framework that unravels the expressive power of spectral-temporal GNNs.
AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetes
This paper discusses horizontal POD resources management in Alibaba Cloud Container Services with a newly deployed AI algorithm framework named AHPA -- the adaptive horizontal pod auto-scaling system.
Robust Dominant Periodicity Detection for Time Series with Missing Data
In this paper, we propose a robust and effective periodicity detection algorithm for time series with block missing data.
DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models
Spatio-temporal graph neural networks (STGNN) have emerged as the dominant model for spatio-temporal graph (STG) forecasting.
AirFormer: Predicting Nationwide Air Quality in China with Transformers
Air pollution is a crucial issue affecting human health and livelihoods, as well as one of the barriers to economic and social growth.
Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment
The goal of sequential event prediction is to estimate the next event based on a sequence of historical events, with applications to sequential recommendation, user behavior analysis and clinical treatment.
TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis
Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data.
TreeDRNet:A Robust Deep Model for Long Term Time Series Forecasting
Various deep learning models, especially some latest Transformer-based approaches, have greatly improved the state-of-art performance for long-term time series forecasting. However, those transformer-based models suffer a severe deterioration performance with prolonged input length, which prohibits them from using extended historical info. Moreover, these methods tend to handle complex examples in long-term forecasting with increased model complexity, which often leads to a significant increase in computation and less robustness in performance(e. g., overfitting).
Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection
Dynamic time warping (DTW) is an effective dissimilarity measure in many time series applications.
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they effectively utilize historical information.
Ranked #3 on
Time Series Forecasting
on ETTh2 (96) Univariate
A Global Modeling Approach for Load Forecasting in Distribution Networks
Efficient load forecasting is needed to ensure better observability in the distribution networks, whereas such forecasting is made possible by an increasing number of smart meter installations.
NetRCA: An Effective Network Fault Cause Localization Algorithm
Localizing the root cause of network faults is crucial to network operation and maintenance.
Transformers in Time Series: A Survey
From the perspective of network structure, we summarize the adaptations and modifications that have been made to Transformers in order to accommodate the challenges in time series analysis.
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
Although Transformer-based methods have significantly improved state-of-the-art results for long-term series forecasting, they are not only computationally expensive but more importantly, are unable to capture the global view of time series (e. g. overall trend).
CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms
As business of Alibaba expands across the world among various industries, higher standards are imposed on the service quality and reliability of big data cloud computing platforms which constitute the infrastructure of Alibaba Cloud.
A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition
Many real-world time series exhibit multiple seasonality with different lengths.
Two-Stage Framework for Seasonal Time Series Forecasting
By incorporating the learned long-range structure, the second stage can enhance the prediction accuracy in the forecast horizon.
Time Series Data Augmentation for Deep Learning: A Survey
In this paper, we systematically review different data augmentation methods for time series.
RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity Detection
Periodicity detection is a crucial step in time series tasks, including monitoring and forecasting of metrics in many areas, such as IoT applications and self-driving database management system.
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks
It is deployed as a public online service and widely adopted in different business scenarios at Alibaba Group.
RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering
Extracting the underlying trend signal is a crucial step to facilitate time series analysis like forecasting and anomaly detection.
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component.
