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 2901–2925 of 6748 papers

TitleStatusHype
Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSxCode1
Analysis of bank leverage via dynamical systems and deep neural networks—0
Weak Form Generalized Hamiltonian LearningCode1
Boosted Embeddings for Time Series Forecasting—0
Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data—0
Deep Transformer Networks for Time Series Classification: The NPP Safety Case—0
Deep Time Series Forecasting with Shape and Temporal CriteriaCode1
DeepSITH: Efficient Learning via Decomposition of What and When Across Time ScalesCode1
Flow-based Spatio-Temporal Structured Prediction of Motion DynamicsCode0
Granger Causality Based Hierarchical Time Series Clustering for State Estimation—0
Neural Network for Weighted Signal Temporal Logic—0
Market Regime Detection via Realized Covariances: A Comparison between Unsupervised Learning and Nonlinear Models—0
Fingerprint Presentation Attack Detection utilizing Time-Series, Color Fingerprint Captures—0
Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTCode1
CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data—0
Min(d)ing the President: A text analytic approach to measuring tax news—0
Bootstrap Inference for Hawkes and General Point Processes—0
Evaluation of Time Series Forecasting Models for Estimation of PM2.5 Levels in Air—0
Towards a Rigorous Evaluation of Explainability for Multivariate Time Series—0
Autoencoder-based Representation Learning from Heterogeneous Multivariate Time Series Data of Mechatronic SystemsCode0
Analysis of bio-electro-chemical signals from passive sweat-based wearable electro-impedance spectroscopy (EIS) towards assessing blood glucose modulations—0
AST: Audio Spectrogram TransformerCode2
Model Compression for Dynamic Forecast CombinationCode0
COHORTNEY: Non-Parametric Clustering of Event Sequences—0
Extraction of instantaneous frequencies and amplitudes in nonstationary time-series dataCode1
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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