Papers Time Series Regression
“Time Series Regression” 태그가 달린 논문 86편 · 필터 해제
CarbonBench: A Global Benchmark for Upscaling of Carbon Fluxes Using Zero-Shot Learning
Accurately quantifying terrestrial carbon exchange is essential for climate policy and carbon accounting, yet models must generalize to ecosystems underrepresented in sparse eddy covariance observations. Despite this cha…
Time Series RegressionZero-Shot LearningTransfer LearningHarmonica: A Self-Adaptation Exemplar for Sustainable MLOps
Machine learning enabled systems (MLS) often operate in settings where they regularly encounter uncertainties arising from changes in their surrounding environment. Without structured oversight, such changes can degrade …
Time Series RegressionTS-HINT: Enhancing Semiconductor Time Series Regression Using Attention Hints From Large Language Model Reasoning
Existing data-driven methods rely on the extraction of static features from time series to approximate the material removal rate (MRR) of semiconductor manufacturing processes such as chemical mechanical polishing (CMP).…
Time Series RegressionFew-Shot LearningWhen, How Long and How Much? Interpretable Neural Networks for Time Series Regression by Learning to Mask and Aggregate
Time series extrinsic regression (TSER) refers to the task of predicting a continuous target variable from an input time series. It appears in many domains, including healthcare, finance, environmental monitoring, and en…
Time Series RegressionFIC-TSC: Learning Time Series Classification with Fisher Information Constraint
Analyzing time series data is crucial to a wide spectrum of applications, including economics, online marketplaces, and human healthcare. In particular, time series classification plays an indispensable role in segmentin…
ClassificationTime SeriesTime Series ClassificationTime Series RegressionLarge Language Model Enhanced Particle Swarm Optimization for Hyperparameter Tuning for Deep Learning Models
Determining the ideal architecture for deep learning models, such as the number of layers and neurons, is a difficult and resource-intensive process that frequently relies on human tuning or computationally costly optimi…
Deep LearningLanguage ModelingLanguage ModellingLarge Language Model+3Rethinking Remaining Useful Life Prediction with Scarce Time Series Data: Regression under Indirect Supervision
Supervised time series prediction relies on directly measured target variables, but real-world use cases such as predicting remaining useful life (RUL) involve indirect supervision, where the target variable is labeled a…
PredictionregressionTime SeriesTime Series Prediction+1Did ChatGPT or Copilot use alter the style of internet news headlines? A time series regression analysis
The release of advanced Large Language Models (LLMs) such as ChatGPT and Copilot is changing the way text is created and may influence the content that we find on the web. This study investigated whether the release of t…
Time SeriesTime Series AnalysisTime Series RegressionTS-Inverse: A Gradient Inversion Attack Tailored for Federated Time Series Forecasting Models
Federated learning (FL) for time series forecasting (TSF) enables clients with privacy-sensitive time series (TS) data to collaboratively learn accurate forecasting models, for example, in energy load prediction. Unfortu…
Federated Learningimage-classificationImage ClassificationTime Series+2A Supervised Screening and Regularized Factor-Based Method for Time Series Forecasting
Factor-based forecasting using Principal Component Analysis (PCA) is an effective machine learning tool for dimension reduction with many applications in statistics, economics, and finance. This paper introduces a Superv…
Dimensionality ReductionTime SeriesTime Series ForecastingTime Series RegressionRecurrent Memory for Online Interdomain Gaussian Processes
We propose a novel online Gaussian process (GP) model that is capable of capturing long-term memory in sequential data in an online regression setting. Our model, Online HiPPO Sparse Variational Gaussian Process Regressi…
Computational EfficiencyGaussian ProcessesregressionTime Series RegressionPreventing Non-intrusive Load Monitoring Privacy Invasion: A Precise Adversarial Attack Scheme for Networked Smart Meters
Smart grid, through networked smart meters employing the non-intrusive load monitoring (NILM) technique, can considerably discern the usage patterns of residential appliances. However, this technique also incurs privacy …
Adversarial AttackNon-Intrusive Load MonitoringTime Series RegressionLLM-ABBA: Understanding time series via symbolic approximation
The success of large language models (LLMs) for time series has been demonstrated in previous work. Utilizing a symbolic time series representation, one can efficiently bridge the gap between LLMs and time series. Howeve…
Time SeriesTime Series ClassificationTime Series PredictionTime Series RegressionQuantized symbolic time series approximation
Time series are ubiquitous in numerous science and engineering domains, e.g., signal processing, bioinformatics, and astronomy. Previous work has verified the efficacy of symbolic time series representation in a variety …
Anomaly DetectionAstronomyQuantizationregression+2Analysis of Potential Biases and Validity of Studies Using Multiverse Approaches to Assess the Impacts of Government Responses to Epidemics
We analyze the methodological approach and validity of interpretation of using national-level time-series regression analyses relating epidemic outcomes to policies that estimate many models involving permutations of ana…
Time Series RegressionvalidChange-Point Detection in Time Series Using Mixed Integer Programming
We use cutting-edge mixed integer optimization (MIO) methods to develop a framework for detection and estimation of structural breaks in time series regression models. The framework is constructed based on the least squa…
Change Point DetectionregressionTime SeriesTime Series RegressionEnhancing Financial Market Predictions: Causality-Driven Feature Selection
This paper introduces the FinSen dataset that revolutionizes financial market analysis by integrating economic and financial news articles from 197 countries with stock market data. The dataset's extensive coverage spans…
ClassificationTime Series RegressionLearning High-Frequency Functions Made Easy with Sinusoidal Positional Encoding
Fourier features based positional encoding (PE) is commonly used in machine learning tasks that involve learning high-frequency features from low-dimensional inputs, such as 3D view synthesis and time series regression w…
regressiontext-to-speechText to SpeechTime Series+1Reinforced Knowledge Distillation for Time Series Regression
As one of the most popular and effective methods in model compression, knowledge distillation (KD) attempts to transfer knowledge from single or multiple large-scale networks (i.e., Teachers) to a compact network (i.e., …
Knowledge DistillationModel CompressionregressionTime Series+1Deciphering public attention to geoengineering and climate issues using machine learning and dynamic analysis
As the conversation around using geoengineering to combat climate change intensifies, it is imperative to engage the public and deeply understand their perspectives on geoengineering research, development, and potential …
ArticlesSentiment AnalysisTime Series Regression