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 1151–1175 of 6748 papers

TitleStatusHype
Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning ApproachCode0
Granger Causality using Neural NetworksCode0
AUTOSHAPE: An Autoencoder-Shapelet Approach for Time Series Clustering—0
Recurrence measures and transitions in stock market dynamics—0
Forecasting Algorithms for Causal Inference with Panel DataCode0
Learning Financial Networks with High-frequency Trade Data—0
Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic ForecastingCode1
COPER: Continuous Patient State PerceiverCode0
Multimodal Brain Disease Classification with Functional Interaction Learning from Single fMRI Volume—0
SA-NET.v2: Real-time vehicle detection from oblique UAV images with use of uncertainty estimation in deep meta-learning—0
Automatic Segmentation of the Placenta in BOLD MRI Time SeriesCode0
Factor Network Autoregressions—0
Metadata-enhanced contrastive learning from retinal optical coherence tomography images—0
Visually Evaluating Generative Adversarial Networks Using Itself under Multivariate Time SeriesCode0
Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data—0
A Deep Learning Approach to Detect Lean Blowout in Combustion Systems—0
EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous VariablesCode0
Detecting Multivariate Time Series Anomalies with Zero Known LabelCode1
Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series—0
Multifractal cross-correlations of bitcoin and ether trading characteristics in the post-COVID-19 time—0
Overlooked Poses Actually Make Sense: Distilling Privileged Knowledge for Human Motion Prediction—0
Concentration inequalities for correlated network-valued processes with applications to community estimation and changepoint analysis—0
MFRFNN: Multi-Functional Recurrent Fuzzy Neural Network for Chaotic Time Series PredictionCode1
Interpretable Time Series Clustering Using Local Explanations—0
Predicting Future Mosquito Larval Habitats Using Time Series Climate Forecasting and Deep LearningCode0
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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