Papers Time Series Analysis
“Time Series Analysis” 태그가 달린 논문 6,854편 · 필터 해제
Beyond Stationarity in Time Series: Discovering Causal Structures and Latent Regimes via Markov Blankets
This paper introduces Regime-aware Constraint-Based and Noise-Based causal discovery with Markov Blankets (RCBNB-MB), a novel causal discovery algorithm for time series that relaxes the common assumption of a single, tim…
Time Series AnalysisDiscretizing Continuous Time Series for Imputation with Masked Diffusion Training
Time series imputation is a crucial area for reliable time series analysis, yet it remains challenging due to the complex temporal dynamics and noise of real-world data. Existing approaches, however, exhibit two limitati…
Time Series AnalysisTimeSage-EV: A Live Benchmark for Agentic Time Series Analysis in Evolving Environments
Time series analysis in high-stakes domains relies on recurring data releases, where new observations can alter the evidence base and the validity of later conclusions. Existing time series QA benchmarks mostly rely on f…
Time Series AnalysisPost-Training in Time Series Foundation Models: A Unifying Framework
Time series foundation models (TSFMs) have emerged as general-purpose models for time series analysis, but pretraining alone is often insufficient for reliable downstream deployment. Bridging this gap requires further in…
Time Series AnalysisScaling Time Series Classification via XAI-Driven Data Reduction
Explainable AI (XAI) for time series has seen significant algorithmic growth, but its utility in providing measurable performance gains for downstream tasks remains under-explored. This paper bridges this gap by introduc…
Time Series ClassificationTime Series AnalysisFeature ImportanceQCNN with Rough Path Signature Kernels
Time series analysis plays a vital role across a wide range of scientific and engineering domains but poses substantial computational challenges. A major difficulty arises from the time reparameterization invariance of t…
Time Series ClassificationBinary ClassificationTime Series AnalysisEnhancing deep learning models for time series classification via knowledge distillation
Deep learning has achieved remarkable success in various domains including time series analysis, computer vision and natural language processing. However, high computational and memory demands of state-of-the-art archite…
Time Series ClassificationKnowledge DistillationTime Series AnalysisZeus: Towards Tuning-Free Foundation Model for Time Series Analysis
We present Zeus, a unified tuning-free Time Series Foundation Model (TSFM) that delivers superior performance across diverse analysis tasks without any task-specific fine-tuning. Unlike prior studies that primarily focus…
Computational EfficiencyTime Series AnalysisUnified Zero-Shot Time Series Forecasting: A Darts Foundation
Since its initial release in 2020, Darts has become a widely used open-source Python library for time series analysis. A series of foundation models have recently claimed accuracy improvements in zero-shot forecasting, p…
Time Series ForecastingTime Series AnalysisForecasting Technological Directions in Wireless Networks and Mobile Computing via AutoML Framework
The exponential increase in scientific publications has driven the emergence of new trends. Accurate forecasting of these developments is essential for researchers and professionals to stay updated with advancements in t…
Time Series AnalysisFrom Recognition to Understanding: Unlocking Cognitive Time Series Reasoning with LLMs
Time series analysis has recently been coupled with Large Language Models (LLMs) to leverage their reasoning and world knowledge capabilities, yet gains remain limited. We attribute this to a fundamental mismatch between…
Time Series AnalysisAI-Driven Assessment of Human Tutors: Linking Training Performance to Real-Life Practice
There exist numerous tutor training platforms. However, few provide AI-driven training and evaluation for human tutors based on real-life performance. We present an AI-driven system that assesses both open responses duri…
Time Series AnalysisTowards Verifiable Agentic Data Science: Solving Irregular TSQA Via Tool-Grounded Reasoning
Time series data in real-world deployments is overwhelmingly irregular. Observations are asynchronous, missing values are informative rather than random, and sampling frequencies vary across sensors and operational windo…
Time Series AnalysisQuestion AnsweringTime Series Analysis in Machine Learning
Time series analysis is a fundamental component of machine learning, especially in astrophysics and cosmology where temporal data abound. This chapter provides a pedagogical review of time series analysis techniques from…
Time Series AnalysisWeather ForecastingGaussian ProcessesGNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series
Displacement time series from Global Navigation Satellite Systems (GNSS) are essential for a wide range of applications, including monitoring tectonic crustal deformations and investigating the different stages of the ea…
Time Series AnalysisTimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning
Time series data inform critical decisions across many real-world domains. While large language model (LLM) agents can analyze data through natural language and tools, it remains unclear whether they can conduct reliable…
Time Series AnalysisAnomaly DetectionDecision MakingTGFormer: Towards Temporal Graph Transformer with Auto-Correlation Mechanism
The growing interest in Temporal Graph Neural Networks (TGNNs) stems from their ability to model complex dynamics and deliver superior performance. However, TGNNs encounter fundamental challenges in capturing long-term d…
Time Series AnalysisGraph LearningUncertainty-DTW for Sequences and Visual Tokens
Aligning structured data is a fundamental problem in computer vision and machine learning, underlying tasks such as time series analysis, human action recognition, and visual representation learning. Existing alignment m…
Representation LearningTime Series AnalysisTemporal SequencesAction RecognitionContinuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models
Token-based time series large language models (TS-LLMs) have emerged as a promising direction for time series analysis and reasoning. However, prior studies largely overlook the inherent continuity and ordinality of time…
Time Series AnalysisGenTS: A Comprehensive Benchmark Library for Generative Time Series Models
Generative models have demonstrated remarkable potential in time series analysis tasks, like synthesis, forecasting, imputation, etc. However, offering limited coverage for generative models, existing time series librari…
Time Series ForecastingTime Series Analysis