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 926–950 of 6748 papers

TitleStatusHype
Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts—0
Interpreting County Level COVID-19 Infection and Feature Sensitivity using Deep Learning Time Series ModelsCode0
Temporal Spatial Decomposition and Fusion Network for Time Series Forecasting—0
From Rules to Regs: A Structural Topic Model of Collusion Research—0
Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural NetworksCode0
Inference on Causal Effects of Interventions in Time using Gaussian Processes—0
Biological neurons act as generalization filters in reservoir computing—0
Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting—0
Transformer-based conditional generative adversarial network for multivariate time series generationCode1
TimesNet: Temporal 2D-Variation Modeling for General Time Series AnalysisCode6
Stock Volatility Prediction using Time Series and Deep Learning Approach—0
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density EstimationCode1
Learning Video-independent Eye Contact Segmentation from In-the-Wild VideosCode0
GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks—0
Feature Importance for Time Series Data: Improving KernelSHAP—0
The Local to Unity Dynamic Tobit Model—0
Efficient probabilistic reconciliation of forecasts for real-valued and count time series—0
Tripletformer for Probabilistic Interpolation of Irregularly sampled Time SeriesCode0
Learning Signal Temporal Logic through Neural Network for Interpretable ClassificationCode1
MTSMAE: Masked Autoencoders for Multivariate Time-Series Forecasting—0
Nonparametric and Regularized Dynamical Wasserstein Barycenters for Sequential Observations—0
Using Entropy Measures for Monitoring the Evolution of Activity Patterns—0
Public Transit Arrival Prediction: a Seq2Seq RNN Approach—0
Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks—0
Connecting Surrogate Safety Measures to Crash Probablity via Causal Probabilistic Time Series Prediction—0
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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