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 401–425 of 6748 papers

TitleStatusHype
Ensemble Conformalized Quantile Regression for Probabilistic Time Series ForecastingCode1
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly DetectionCode1
Adaptive Conformal Predictions for Time SeriesCode1
Evaluation of post-hoc interpretability methods in time-series classificationCode1
Motion Correction and Volumetric Reconstruction for Fetal Functional Magnetic Resonance Imaging DataCode1
Detecting Anomalies within Time Series using Local Neural TransformationsCode1
Time Series Anomaly Detection by Cumulative Radon FeaturesCode1
TACTiS: Transformer-Attentional Copulas for Time SeriesCode1
Structured Time Series Prediction without Structural PriorCode1
Advanced sleep spindle identification with neural networksCode1
Decoupling Local and Global Representations of Time SeriesCode1
Skeleton-Based Action Segmentation with Multi-Stage Spatial-Temporal Graph Convolutional Neural NetworksCode1
Methodology for forecasting and optimization in IEEE-CIS 3rd Technical ChallengeCode1
Similarity Learning based Few Shot Learning for ECG Time Series ClassificationCode1
Time-Series Anomaly Detection with Implicit Neural RepresentationCode1
Neural Information Squeezer for Causal EmergenceCode1
SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud RemovalCode1
SwinUNet3D -- A Hierarchical Architecture for Deep Traffic Prediction using Shifted Window TransformersCode1
Continual Transformers: Redundancy-Free Attention for Online InferenceCode1
An efficient aggregation method for the symbolic representation of temporal dataCode1
Time Series Generation with Masked AutoencoderCode1
Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series ForecastingCode1
Generative time series models using Neural ODE in Variational AutoencodersCode1
Classification of Long Sequential Data using Circular Dilated Convolutional Neural NetworksCode1
Towards Similarity-Aware Time-Series ClassificationCode1
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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