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 301–325 of 6748 papers

TitleStatusHype
Detecting Multivariate Time Series Anomalies with Zero Known LabelCode1
MFRFNN: Multi-Functional Recurrent Fuzzy Neural Network for Chaotic Time Series PredictionCode1
ClaSP -- Parameter-free Time Series SegmentationCode1
A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecastingCode1
Benchmark time series data sets for PyTorch -- the torchtime packageCode1
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series ClassificationCode1
CODiT: Conformal Out-of-Distribution Detection in Time-Series DataCode1
Respecting Time Series Properties Makes Deep Time Series Forecasting PerfectCode1
Transfer learning for time series classification using synthetic data generationCode1
Spatiotemporal Propagation Learning for Network-Wide Flight Delay PredictionCode1
A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vesselsCode1
Memory-free Online Change-point Detection: A Novel Neural Network ApproachCode1
Don't Pay Attention to the Noise: Learning Self-supervised Representations of Light Curves with a Denoising Time Series TransformerCode1
Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical SystemsCode1
Deep Contrastive One-Class Time Series Anomaly DetectionCode1
Multivariate Time Series Anomaly Detection with Few Positive SamplesCode1
Color-aware two-branch DCNN for efficient plant disease classificationCode1
Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEsCode1
Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series ForecastingCode1
TTS-CGAN: A Transformer Time-Series Conditional GAN for Biosignal Data AugmentationCode1
Local Evaluation of Time Series Anomaly Detection AlgorithmsCode1
Multi-Variate Time Series Forecasting on Variable SubsetsCode1
Utilizing Expert Features for Contrastive Learning of Time-Series RepresentationsCode1
An Open Source and Reproducible Implementation of LSTM and GRU Networks for Time Series ForecastingCode1
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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