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 576–600 of 6748 papers

TitleStatusHype
Calibration of Google Trends Time SeriesCode1
Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization ApproachCode1
Axial-LOB: High-Frequency Trading with Axial AttentionCode1
Forecasting in Non-stationary Environments with Fuzzy Time SeriesCode1
Hierarchical forecasting with a top-down alignment of independent level forecastsCode1
Bayesian hierarchical stacking: Some models are (somewhere) usefulCode1
Bayesian Neural Architecture Search using A Training-Free Performance MetricCode1
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODECode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
A spatio-temporal LSTM model to forecast across multiple temporal and spatial scalesCode1
Benchmarking Deep Learning Interpretability in Time Series PredictionsCode1
Bilinear Input Normalization for Neural Networks in Financial ForecastingCode1
From Fourier to Koopman: Spectral Methods for Long-term Time Series PredictionCode1
Benchmark time series data sets for PyTorch -- the torchtime packageCode1
From Time Series to Networks in R with the ts2net PackageCode1
Enhancing Multivariate Time Series Classifiers through Self-Attention and Relative Positioning InfusionCode1
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
Gaussian Process Prior Variational AutoencodersCode1
Generalised Interpretable Shapelets for Irregular Time SeriesCode1
Accelerating Recurrent Neural Networks for Gravitational Wave ExperimentsCode1
BolT: Fused Window Transformers for fMRI Time Series AnalysisCode1
Generative ODE Modeling with Known UnknownsCode1
Ensemble Conformalized Quantile Regression for Probabilistic Time Series ForecastingCode1
ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor dataCode1
Efficient recurrent architectures through activity sparsity and sparse back-propagation through timeCode1
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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