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 801–825 of 6748 papers

TitleStatusHype
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
Price graphs: Utilizing the structural information of financial time series for stock predictionCode1
BolT: Fused Window Transformers for fMRI Time Series AnalysisCode1
Bilinear Input Normalization for Neural Networks in Financial ForecastingCode1
Probabilistic Recurrent State-Space ModelsCode1
Probabilistic Time Series Forecasting with Structured Shape and Temporal DiversityCode1
PromptCast: A New Prompt-based Learning Paradigm for Time Series ForecastingCode1
Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological DataCode1
A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy GridsCode1
A Time-dependent SIR model for COVID-19 with Undetectable Infected PersonsCode1
Building an Automated and Self-Aware Anomaly Detection SystemCode1
Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and ForecastingCode1
PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed GraphsCode1
pyWATTS: Python Workflow Automation Tool for Time SeriesCode1
Real-Time Anomaly Detection and Feature Analysis Based on Time Series for Surveillance VideoCode1
Reconstructing dynamics from sparse observations with no training on target systemCode1
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature SpacesCode1
Recurrent Neural Networks for Multivariate Time Series with Missing ValuesCode1
ReFocus: Reinforcing Mid-Frequency and Key-Frequency Modeling for Multivariate Time Series ForecastingCode1
Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series ForecastingCode1
Reservoir Computing meets Recurrent Kernels and Structured TransformsCode1
Calibration of Google Trends Time SeriesCode1
BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief PropagationCode1
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systemsCode1
Benchmarking Deep Learning Interpretability in Time Series PredictionsCode1
Show:102550
← PrevPage 33 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