SOTAVerified

Time Series Analysis

Time Series Analysis is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.

( Image credit: Autoregressive CNNs for Asynchronous Time Series )

Papers

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Showing 151–200 of 6748 papers

TitleStatusHype
Large Pre-trained time series models for cross-domain Time series analysis tasksCode1
Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-SeriesCode1
A Multi-Scale Decomposition MLP-Mixer for Time Series AnalysisCode1
OBSUM: An object-based spatial unmixing model for spatiotemporal fusion of remote sensing imagesCode1
Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential ReconstructionCode1
Human Activity Segmentation Challenge @ ECML/PKDD’23Code1
Market-GAN: Adding Control to Financial Market Data Generation with Semantic ContextCode1
Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization ApproachCode1
Network Traffic Classification based on Single Flow Time Series AnalysisCode1
BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief PropagationCode1
Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal BootstrappingCode1
Improving Position Encoding of Transformers for Multivariate Time Series ClassificationCode1
SMPConv: Self-moving Point Representations for Continuous ConvolutionCode1
OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And ForecastingCode1
Time Series Segmentation Applied to a New Data Set for Mobile Sensing of Human ActivitiesCode1
UniTS: A Universal Time Series Analysis Framework Powered by Self-Supervised Representation LearningCode1
Spacecraft Anomaly Detection with Attention Temporal Convolution NetworkCode1
TSMixer: An All-MLP Architecture for Time Series ForecastingCode1
Vector Quantized Time Series Generation with a Bidirectional Prior ModelCode1
Synthetic ECG Signal Generation using Probabilistic Diffusion ModelsCode1
TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked AutoencodersCode1
Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative NetworkCode1
A Synthetic Texas Power System with Time-Series Weather-Dependent Spatiotemporal ProfilesCode1
LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation -- Extended VersionCode1
Set Features for Fine-grained Anomaly DetectionCode1
LightCTS: A Lightweight Framework for Correlated Time Series ForecastingCode1
Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series ForecastingCode1
Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather StationsCode1
MVMTnet: A Multi-variate Multi-modal Transformer for Multi-class Classification of Cardiac Irregularities Using ECG Waveforms and Clinical NotesCode1
Exploring the Advantages of Transformers for High-Frequency TradingCode1
Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional StrategiesCode1
FrAug: Frequency Domain Augmentation for Time Series ForecastingCode1
DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series DataCode1
Forecasting with Deep LearningCode1
PAAPLoss: A Phonetic-Aligned Acoustic Parameter Loss for Speech EnhancementCode1
Temporal Graph Neural Networks for Irregular DataCode1
A Neural PDE Solver with Temporal Stencil ModelingCode1
Improved Online Conformal Prediction via Strongly Adaptive Online LearningCode1
CUTS: Neural Causal Discovery from Irregular Time-Series DataCode1
Enhancing Multivariate Time Series Classifiers through Self-Attention and Relative Positioning InfusionCode1
One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular DataCode1
Weakly Supervised Anomaly Detection: A SurveyCode1
DeepVATS: Deep Visual Analytics for Time SeriesCode1
ASTRIDE: Adaptive Symbolization for Time Series DatabasesCode1
Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining ApproachCode1
Domain Adaptation for Time Series Under Feature and Label ShiftsCode1
Deep Learning for Time Series Classification and Extrinsic Regression: A Current SurveyCode1
Window Size Selection in Unsupervised Time Series Analytics: A Review and BenchmarkCode1
SimMTM: A Simple Pre-Training Framework for Masked Time-Series ModelingCode1
Recurrences reveal shared causal drivers of complex time seriesCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47—Unverified
2GRU-D - APC (n = 1)F127.3—Unverified
3GRU-APC (n = 1)F125.7—Unverified
4GRU-DF122.5—Unverified
5GRUF122.3—Unverified
6GRU-SimpleF122.2—Unverified
7GRU-MeanF122.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51—Unverified
2ViT% Test Accuracy98.11—Unverified
3FlexTCN-4% Test Accuracy97.73—Unverified
4MatchboxNet% Test Accuracy97.4—Unverified
5CKCNN (100k)% Test Accuracy95.27—Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73—Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13—Unverified