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 3001–3025 of 6748 papers

TitleStatusHype
Long Short-term Cognitive NetworksCode0
National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?—0
MissFormer: (In-)attention-based handling of missing observations for trajectory filtering and prediction—0
Attaining entropy production and dissipation maps from Brownian movies via neural networksCode0
As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learningCode0
FallDeF5: A Fall Detection Framework Using 5G-based Deep Gated Recurrent Unit Networks—0
Evolving-Graph Gaussian ProcessesCode0
Improved Prediction and Network Estimation Using the Monotone Single Index Multi-variate Autoregressive Model—0
False Negative Reduction in Video Instance Segmentation using Uncertainty EstimatesCode0
Capturing the temporal constraints of gradual patternsCode0
On a novel training algorithm for sequence-to-sequence predictive recurrent networks—0
Autonomous Deep Quality Monitoring in Streaming EnvironmentsCode0
Functional Classwise Principal Component Analysis: A Novel Classification Framework—0
The mbsts package: Multivariate Bayesian Structural Time Series Models in R—0
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model—0
Condition Monitoring of Drive Trains by Data Fusion of Acoustic Emission and Vibration SensorsCode0
Temporal Graph Signal Decomposition—0
Neural ODE to model and prognose thermoacoustic instability—0
Domain-guided Machine Learning for Remotely Sensed In-Season Crop Growth Estimation—0
Beyond Predictions in Neural ODEs: Identification and Interventions—0
Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection—0
MegazordNet: combining statistical and machine learning standpoints for time series forecasting—0
Approximate Bayesian Computation with Path SignaturesCode0
Machine learning structure preserving brackets for forecasting irreversible processes—0
Residual Networks as Flows of Velocity Fields for Diffeomorphic Time Series Alignment—0
Show:102550
← PrevPage 121 of 270Next →
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