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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 751–775 of 6748 papers

TitleStatusHype
Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series ForecastingCode1
Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor DataCode1
Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural Network and Variational Mode DecompositionCode1
Multitask learning and benchmarking with clinical time series dataCode1
A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking ApplicationsCode1
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
Multivariate Time Series Anomaly Detection with Few Positive SamplesCode1
Multivariate Time Series Forecasting with Transfer Entropy GraphCode1
A Deep Learning Approach to Analyzing Continuous-Time SystemsCode1
Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic DataCode1
NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series ForecastingCode1
catch22: CAnonical Time-series CHaracteristicsCode1
Network Traffic Classification based on Single Flow Time Series AnalysisCode1
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality ModelingCode1
CAMul: Calibrated and Accurate Multi-view Time-Series ForecastingCode1
Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSxCode1
Can LLMs Understand Time Series Anomalies?Code1
Causal Forecasting:Generalization Bounds for Autoregressive ModelsCode1
Neural Jump Stochastic Differential EquationsCode1
ASTRIDE: Adaptive Symbolization for Time Series DatabasesCode1
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time seriesCode1
An Accurate and Fully-Automated Ensemble Model for Weekly Time Series ForecastingCode1
Neural ODEs as Feedback Policies for Nonlinear Optimal ControlCode1
An Empirical Study of Graph-Based Approaches for Semi-Supervised Time Series ClassificationCode1
CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial LearningCode1
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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