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 2101–2125 of 6748 papers

TitleStatusHype
TimeVAE: A Variational Auto-Encoder for Multivariate Time Series GenerationCode1
Learning Graph Neural Networks for Multivariate Time Series Anomaly DetectionCode1
On Sparse High-Dimensional Graphical Model Learning For Dependent Time Series—0
Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning ApproachCode1
Forecasting Crude Oil Price Using Event Extraction—0
Decoding Causality by Fictitious VAR Modeling—0
LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts—0
Nyström Regularization for Time Series Forecasting—0
Evaluating Contrastive Learning on Wearable Timeseries for Downstream Clinical Outcomes—0
A Time-Series Scale Mixture Model of EEG with a Hidden Markov Structure for Epileptic Seizure Detection—0
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting—0
GraSSNet: Graph Soft Sensing Neural Networks—0
Soft Sensing Model Visualization: Fine-tuning Neural Network from What Model Learned—0
Identifying On-road Scenarios Predictive of ADHD usingDriving Simulator Time Series Data—0
Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series—0
Observation Error Covariance Specification in Dynamical Systems for Data assimilation using Recurrent Neural Networks—0
Model-Based Reinforcement Learning via Stochastic Hybrid Models—0
Exploiting the Power of Levenberg-Marquardt Optimizer with Anomaly Detection in Time Series—0
Soft Sensing Transformer: Hundreds of Sensors are Worth a Single WordCode0
Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility informationCode0
American Hate Crime Trends Prediction with Event Extraction—0
Learning from Multiple Time Series: A Deep Disentangled Approach to Diversified Time Series Forecasting—0
ARISE: ApeRIodic SEmi-parametric Process for Efficient Markets without Periodogram and Gaussianity Assumptions—0
A toolkit for data-driven discovery of governing equations in high-noise regimesCode0
A Comparison of Model-Free and Model Predictive Control for Price Responsive Water Heaters—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