Papers Time Series
“Time Series” 태그가 달린 논문 9,169편 · 필터 해제
MoTM: Towards a Foundation Model for Time Series Imputation based on Continuous Modeling
Recent years have witnessed a growing interest for time series foundation models, with a strong emphasis on the forecasting task. Yet, the crucial task of out-of-domain imputation of missing values remains largely undere…
Domain GeneralizationImputationMissing ValuesTime SeriesThe Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting
Transformer-based models have recently become dominant in Long-term Time Series Forecasting (LTSF), yet the variations in their architecture, such as encoder-only, encoder-decoder, and decoder-only designs, raise a cruci…
DecoderTime SeriesTime Series ForecastingData Augmentation in Time Series Forecasting through Inverted Framework
Currently, iTransformer is one of the most popular and effective models for multivariate time series (MTS) forecasting. Thanks to its inverted framework, iTransformer effectively captures multivariate correlation. Howeve…
Data AugmentationTime SeriesTime Series ForecastingD3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data
With advancements in computing and communication technologies, the Internet of Things (IoT) has seen significant growth. IoT devices typically collect data from various sensors, such as temperature, humidity, and energy …
Federated LearningTime SeriesWeather ForecastingTowards Interpretable Time Series Foundation Models
In this paper, we investigate the distillation of time series reasoning capabilities into small, instruction-tuned language models as a step toward building interpretable time series foundation models. Leveraging a synth…
Time SeriesMoFE-Time: Mixture of Frequency Domain Experts for Time-Series Forecasting Models
As a prominent data modality task, time series forecasting plays a pivotal role in diverse applications. With the remarkable advancements in Large Language Models (LLMs), the adoption of LLMs as the foundational architec…
Mixture-of-ExpertsTime SeriesTime Series ForecastingFoundation models for time series forecasting: Application in conformal prediction
The zero-shot capabilities of foundation models (FMs) for time series forecasting offer promising potentials in conformal prediction, as most of the available data can be allocated to calibration. This study compares the…
Conformal PredictionPredictionPrediction IntervalsTime Series+1Bridging the Last Mile of Prediction: Enhancing Time Series Forecasting with Conditional Guided Flow Matching
Diffusion models, a type of generative model, have shown promise in time series forecasting. But they face limitations like rigid source distributions and limited sampling paths, which hinder their performance. Flow matc…
Time SeriesTime Series ForecastingPredicting Graph Structure via Adapted Flux Balance Analysis
Many dynamic processes such as telecommunication and transport networks can be described through discrete time series of graphs. Modelling the dynamics of such time series enables prediction of graph structure at future …
PredictionTime SeriesTime Series PredictionDecomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection
With the advent of Transformers, time series forecasting has seen significant advances, yet it remains challenging due to the need for effective sequence representation, memory construction, and accurate target projectio…
Computational EfficiencyTime SeriesTime Series Forecastingskfolio: Portfolio Optimization in Python
Portfolio optimization is a fundamental challenge in quantitative finance, requiring robust computational tools that integrate statistical rigor with practical implementation. We present skfolio, an open-source Python li…
ManagementPortfolio OptimizationTime SeriesStochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning
Neuromorphic and quantum computing have recently emerged as promising paradigms for advancing artificial intelligence, each offering complementary strengths. Neuromorphic systems built on spiking neurons excel at process…
Time SeriesProcess mining-driven modeling and simulation to enhance fault diagnosis in cyber-physical systems
Fault diagnosis in Cyber-Physical Systems (CPSs) is essential for ensuring system dependability and operational efficiency by accurately detecting anomalies and identifying their root causes. However, the manual modeling…
Anomaly DetectionFault DiagnosisTime SeriesTime Series AnalysismTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale
Multivariate time series anomaly detection (MTS-AD) is critical in domains like healthcare, cybersecurity, and industrial monitoring, yet remains challenging due to complex inter-variable dependencies, temporal dynamics,…
Anomaly DetectionBenchmarkingLanguage ModelingLanguage Modelling+4SEED: A Structural Encoder for Embedding-Driven Decoding in Time Series Prediction with LLMs
Multivariate time series forecasting requires models to simultaneously capture variable-wise structural dependencies and generalize across diverse tasks. While structural encoders are effective in modeling feature intera…
Language ModelingLanguage ModellingMultivariate Time Series ForecastingRepresentation Learning+3Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios
Forecasting attracts a lot of research attention in the electricity value chain. However, most studies concentrate on short-term forecasting of generation or consumption with a focus on systems and less on individual con…
DenoisingGenerative Adversarial NetworkTime SeriesA foundation model with multi-variate parallel attention to generate neuronal activity
Learning from multi-variate time-series with heterogeneous channel configurations remains a fundamental challenge for deep neural networks (DNNs), particularly in clinical domains such as intracranial electroencephalogra…
Seizure DetectionTime SeriesTime Series ForecastingITFormer: Bridging Time Series and Natural Language for Multi-Modal QA with Large-Scale Multitask Dataset
Time-series data are critical in diverse applications, such as industrial monitoring, medical diagnostics, and climate research. However, effectively integrating these high-dimensional temporal signals with natural langu…
Computational EfficiencyQuestion AnsweringTime SeriesOn the ability of Deep Neural Networks to Learn Granger Causality in Multi-Variate Time Series Data
Granger Causality (GC) offers an elegant statistical framework to study the association between multivariate time series data. Linear Vector Autoregressive models (VAR) though have nice interpretation properties but have…
Time SeriesVariable SelectionBenchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series
Anomaly detection in multivariate time series is an important problem across various fields such as healthcare, financial services, manufacturing or physics detector monitoring. Accurately identifying when unexpected err…
Anomaly DetectionBenchmarkingTime SeriesTime Series Anomaly Detection