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 5176–5200 of 6748 papers

TitleStatusHype
Fused Lasso for Feature Selection using Structural Information—0
Deep MR Fingerprinting with total-variation and low-rank subspace priors—0
Forecasting intracranial hypertension using multi-scale waveform metrics—0
Market efficiency, liquidity, and multifractality of Bitcoin: A dynamic study—0
Short-term Road Traffic Prediction based on Deep Cluster at Large-scale Networks—0
E-LSTM-D: A Deep Learning Framework for Dynamic Network Link PredictionCode0
Fault Diagnosis Method Based on Scaling Law for On-line Refrigerant Leak Detection—0
Convolutional-Sparse-Coded Dynamic Mode Decomposition and Its Application to River State Estimation—0
Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI—0
Approximating Continuous Functions on Persistence Diagrams Using Template FunctionsCode0
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection—0
Robust and fast heart rate variability analysis of long and noisy electrocardiograms using neural networks and images—0
KINN: Incorporating Expert Knowledge in Neural Networks—0
Learning to Adaptively Scale Recurrent Neural Networks—0
Actions Generation from Captions—0
WaveletAE: A Wavelet-enhanced Autoencoder for Wind Turbine Blade Icing DetectionCode0
Quick and Easy Time Series Generation with Established Image-based GANs—0
Generalisation in fully-connected neural networks for time series forecasting—0
Sinkhorn Divergence of Topological Signature Estimates for Time Series ClassificationCode0
The Many-to-Many Mapping Between the Concordance Correlation Coefficient and the Mean Square ErrorCode0
Risk Prediction of Peer-to-Peer Lending Market by a LSTM Model with Macroeconomic Factor—0
RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter—0
Machine Learning of Time Series Using Time-delay Embedding and Precision Annealing—0
Weighted Tensor Completion for Time-Series Causal InferenceCode0
Adversarial Generation of Time-Frequency Features with application in audio synthesisCode0
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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