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 6001–6025 of 6748 papers

TitleStatusHype
Accounting for Temporal Variability in Functional Magnetic Resonance Imaging Improves Prediction of IntelligenceCode0
VNIbCReg: VICReg with Neighboring-Invariance and better-Covariance Evaluated on Non-stationary Seismic Signal Time SeriesCode0
The Many-to-Many Mapping Between the Concordance Correlation Coefficient and the Mean Square ErrorCode0
Riesz-Quincunx-UNet Variational Auto-Encoder for Satellite Image DenoisingCode0
Latent Variable Time-varying Network InferenceCode0
On Mini-Batch Training with Varying Length Time SeriesCode0
RIPPLE: Concept-Based Interpretation for Raw Time Series Models in EducationCode0
Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasksCode0
On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM SignalsCode0
Graph Gamma Process Generalized Linear Dynamical SystemsCode0
Deep Learning Detection of Inaccurate Smart Electricity Meters: A Case StudyCode0
On Periodicity Detection and Structural Periodic SimilarityCode0
A Subspace Method for Time Series Anomaly Detection in Cyber-Physical SystemsCode0
Learnable Dynamic Temporal Pooling for Time Series ClassificationCode0
Learnable Group Transform For Time-SeriesCode0
Learnable Path in Neural Controlled Differential EquationsCode0
Topological Machine Learning for Multivariate Time SeriesCode0
On projection methods for functional time series forecastingCode0
A Deep Dive into Perturbations as Evaluation Technique for Time Series XAICode0
TSViz: Demystification of Deep Learning Models for Time-Series AnalysisCode0
RNN-based counterfactual prediction, with an application to homestead policy and public schoolingCode0
Time-Series Event Prediction with Evolutionary State GraphCode0
Correlated daily time series and forecasting in the M4 competitionCode0
Adversarial Generation of Time-Frequency Features with application in audio synthesisCode0
Learning CHARME models with neural networksCode0
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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