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 6526–6550 of 6748 papers

TitleStatusHype
Probabilistic sequential matrix factorizationCode0
Probabilistic Spatial Transformer NetworksCode0
Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising ModelCode0
Temporal Attention augmented Bilinear Network for Financial Time-Series Data AnalysisCode0
Model Selection for Time Series Forecasting: Empirical Analysis of Different EstimatorsCode0
Model selection in reconciling hierarchical time seriesCode0
SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating MechanismsCode0
Temporal Attention Augmented Bilinear Network for Financial Time Series Data AnalysisCode0
Deep Imbalanced Time-series Forecasting via Local Discrepancy DensityCode0
Evaluating time series forecasting models: An empirical study on performance estimation methodsCode0
Deep Hedging: Learning to Simulate Equity Option MarketsCode0
DeepGraviLens: a Multi-Modal Architecture for Classifying Gravitational Lensing DataCode0
Classification of Time-Series Data Using Boosted Decision TreesCode0
AutoFITS: Automatic Feature Engineering for Irregular Time SeriesCode0
A Novel Skeleton-Based Human Activity Discovery Using Particle Swarm Optimization with Gaussian MutationCode0
Probability Paths and the Structure of Predictions over TimeCode0
Classification of simulated radio signals using Wide Residual Networks for use in the search for extra-terrestrial intelligenceCode0
VenoMave: Targeted Poisoning Against Speech RecognitionCode0
The CoSTAR Block Stacking Dataset: Learning with Workspace ConstraintsCode0
Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver BehaviourCode0
Evaluating Short-Term Forecasting of Multiple Time Series in IoT EnvironmentsCode0
Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series ClassificationCode0
Process Model Forecasting Using Time Series Analysis of Event Sequence DataCode0
Autoencoder-based Representation Learning from Heterogeneous Multivariate Time Series Data of Mechatronic SystemsCode0
Weighted Tensor Completion for Time-Series Causal InferenceCode0
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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