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 201–225 of 6748 papers

TitleStatusHype
CUTS: Neural Causal Discovery from Irregular Time-Series DataCode1
Arbitrage-free neural-SDE market modelsCode1
Copula Conformal Prediction for Multi-step Time Series ForecastingCode1
COT-GAN: Generating Sequential Data via Causal Optimal TransportCode1
ADformer: A Multi-Granularity Transformer for EEG-Based Alzheimer's Disease AssessmentCode1
Are we certain it's anomalous?Code1
A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic PredictionCode1
Continuous Latent Process FlowsCode1
Continuous-Time Deep Glioma Growth ModelsCode1
Adjusting for Autocorrelated Errors in Neural Networks for Time SeriesCode1
A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vesselsCode1
A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learningCode1
Construe: a software solution for the explanation-based interpretation of time seriesCode1
Deep and Confident Prediction for Time Series at UberCode1
An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural NetworksCode1
Continual Transformers: Redundancy-Free Attention for Online InferenceCode1
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality ModelingCode1
Advancing the State-of-the-Art for ECG Analysis through Structured State Space ModelsCode1
ASTRIDE: Adaptive Symbolization for Time Series DatabasesCode1
Deep Explicit Duration Switching Models for Time SeriesCode1
An Empirical Study of Graph-Based Approaches for Semi-Supervised Time Series ClassificationCode1
An Accurate and Fully-Automated Ensemble Model for Weekly Time Series ForecastingCode1
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential EquationsCode1
An Experimental Review on Deep Learning Architectures for Time Series ForecastingCode1
An Evaluation of Change Point Detection AlgorithmsCode1
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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