Papers Time Series Prediction
“Time Series Prediction” 태그가 달린 논문 528편 · 필터 해제
Nonlinear Bias-Compensated Adaptive Filter and Its Application for Time-Series Prediction
Most existing nonlinear adaptive filtering algorithms only account for output noise, neglecting the fact that input noise is also prevalent in practice. Although the recently proposed bias-compensated kernel least mean s…
Time Series PredictionReDiTT: Retrieval Augmented Conditional Diffusion Transformers for Asynchronous Time Series
We present a diffusion based model for asynchronous time series prediction, where the goal is to predict the next inter event time and event type. To address the inherent uncertainty of future events, we introduce ReDiTT…
Time Series PredictionMissingness as Signal: Channel-Independent Spectrogram Learning for Clinical Time Series Prediction
Clinical time series prediction in intensive care units remains challenging due to heterogeneous physiological variables and informative missingness. The presence or absence of a measurement can reflect clinical decision…
Time Series PredictionReTAMamba: Reliability-Aware Temporal Aggregation with Mamba for Irregular Clinical Time Series Prediction
Clinical time-series data are difficult to model with methods designed for regular sequences because they exhibit irregular sampling, frequent missing values, and heterogeneous observation patterns across variables. Exis…
Time Series PredictionRareCP: Regime-Aware Retrieval for Efficient Conformal Prediction
Recent advances in uncertainty quantification for time series forecasting show that conformal prediction can provide reliable prediction intervals, yet standard conformal methods are often inefficient under temporal depe…
Time Series ForecastingTime Series PredictionTailedTS: Benchmark Dataset for Heavy-Tailed Time Series Prediction and Periodicity Quantification
We present TailedTS, a large-scale benchmark dataset derived from Wikipedia hourly page view observations throughout 2024, specifically designed to test time series forecasting models under heavy-tailed, zero-inflated, a…
Time Series ForecastingTime Series PredictionSPaRSe-TIME: Saliency-Projected Low-Rank Temporal Modeling for Efficient and Interpretable Time Series Prediction
Time series forecasting is traditionally dominated by sequence-based architectures such as recurrent neural networks and attention mechanisms, which process all time steps uniformly and often incur substantial computatio…
Time Series ForecastingTime Series PredictionThe CTLNet for Shanghai Composite Index Prediction
Shanghai Composite Index prediction has become a hot issue for many investors and academic researchers. Deep learning models are widely applied in multivariate time series forecasting, including recurrent neural networks…
Multivariate Time Series ForecastingTime Series PredictionLearning to Query History: Nonstationary Classification via Learned Retrieval
Nonstationarity is ubiquitous in practical classification settings, leading deployed models to perform poorly even when they generalize well to holdout sets available at training time. We address this by reframing nonsta…
Time Series PredictionOptimizing Hospital Capacity During Pandemics: A Dual-Component Framework for Strategic Patient Relocation
The COVID-19 pandemic has placed immense strain on hospital systems worldwide, leading to critical capacity challenges. This research proposes a two-part framework to optimize hospital capacity through patient relocation…
Time Series PredictionBrainCast: A Spatio-Temporal Forecasting Model for Whole-Brain fMRI Time Series Prediction
Functional magnetic resonance imaging (fMRI) enables noninvasive investigation of brain function, while short clinical scan durations, arising from human and non-human factors, usually lead to reduced data quality and li…
Time Series ForecastingTime Series PredictionDeep Learning for Financial Time Series: A Large-Scale Benchmark of Risk-Adjusted Performance
We present a large scale benchmark of modern deep learning architectures for a financial time series prediction and position sizing task, with a primary focus on Sharpe ratio optimization. Evaluating linear models, recur…
Computational EfficiencyTime Series PredictionRetrodictive Forecasting: A Proof-of-Concept for Exploiting Temporal Asymmetry in Time Series Prediction
We propose a retrodictive forecasting paradigm for time series: instead of predicting the future from the past, we identify the future that best explains the observed present via inverse MAP optimization over a Condition…
Time Series PredictionKernel-based optimization of measurement operators for quantum reservoir computers
Finding optimal measurement operators is crucial for the performance of quantum reservoir computers (QRCs), since they employ a fixed quantum feature map. We formulate the training of both stateless (quantum extreme lear…
Quantum Machine LearningTime Series PredictionImage ClassificationStable Time Series Prediction of Enterprise Carbon Emissions Based on Causal Inference
Against the backdrop of ongoing carbon peaking and carbon neutrality goals, accurate prediction of enterprise carbon emission trends constitutes an essential foundation for energy structure optimization and low-carbon tr…
Time Series PredictionCausal InferenceDA-SPS: A Dual-stage Network based on Singular Spectrum Analysis, Patching-strategy and Spearman-correlation for Multivariate Time-series Prediction
Multivariate time-series forecasting, as a typical problem in the field of time series prediction, has a wide range of applications in weather forecasting, traffic flow prediction, and other scenarios. However, existing …
Time Series PredictionWeather ForecastingMulti-Modal Time Series Prediction via Mixture of Modulated Experts
Real-world time series exhibit complex and evolving dynamics, making accurate forecasting extremely challenging. Recent multi-modal forecasting methods leverage textual information such as news reports to improve predict…
Time Series PredictionAWGformer: Adaptive Wavelet-Guided Transformer for Multi-Resolution Time Series Forecasting
Time series forecasting requires capturing patterns across multiple temporal scales while maintaining computational efficiency. This paper introduces AWGformer, a novel architecture that integrates adaptive wavelet decom…
Computational EfficiencyTime Series ForecastingTime Series PredictionFrom Numbers to Prompts: A Cognitive Symbolic Transition Mechanism for Lightweight Time-Series Forecasting
Large language models have achieved remarkable success in time series prediction tasks, but their substantial computational and memory requirements limit deployment on lightweight platforms. In this paper, we propose the…
Time Series PredictionPrompt EngineeringMODE: Efficient Time Series Prediction with Mamba Enhanced by Low-Rank Neural ODEs
Time series prediction plays a pivotal role across diverse domains such as finance, healthcare, energy systems, and environmental modeling. However, existing approaches often struggle to balance efficiency, scalability, …
Computational EfficiencyTime Series Prediction