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

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Showing 1801–1850 of 6748 papers

TitleStatusHype
Time is limited on the road to asymptopia—0
NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education—0
Progressive Cross-modal Knowledge Distillation for Human Action Recognition—0
ODformer: Spatial-Temporal Transformers for Long Sequence Origin-Destination Matrix Forecasting Against Cross Application Scenario—0
Expressivity of Hidden Markov Chains vs. Recurrent Neural Networks from a system theoretic viewpoint—0
The Counterfactual-Shapley Value: Attributing Change in System Metrics—0
Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market—0
Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives—0
Grasping Core Rules of Time Series through Pure Models—0
Confidence-Guided Learning Process for Continuous Classification of Time Series—0
An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain InjuryCode0
Uncertainty Quantification for Traffic Forecasting: A Unified Approach—0
HyperTime: Implicit Neural Representation for Time Series—0
Real-Time Massive MIMO Channel Prediction: A Combination of Deep Learning and NeuralProphet—0
Approximate Extraction of Late-Time Returns via Morphological Component Analysis—0
Towards Coupling Full-disk and Active Region-based Flare Prediction for Operational Space Weather ForecastingCode0
Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy—0
Quantification of metabolic niche occupancy dynamics in a Baltic Sea bacterial community—0
Representation learning of rare temporal conditions for travel time prediction—0
Estimating Sunlight Using GNSS Signal Strength from Smartphone—0
Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning ApproachCode0
Liquid State Machine-Empowered Reflection Tracking in RIS-Aided THz Communications—0
Granger Causality using Neural NetworksCode0
AUTOSHAPE: An Autoencoder-Shapelet Approach for Time Series Clustering—0
Forecasting Algorithms for Causal Inference with Panel DataCode0
Recurrence measures and transitions in stock market dynamics—0
Learning Financial Networks with High-frequency Trade Data—0
COPER: Continuous Patient State PerceiverCode0
Multimodal Brain Disease Classification with Functional Interaction Learning from Single fMRI Volume—0
Automatic Segmentation of the Placenta in BOLD MRI Time SeriesCode0
Metadata-enhanced contrastive learning from retinal optical coherence tomography images—0
Visually Evaluating Generative Adversarial Networks Using Itself under Multivariate Time SeriesCode0
Factor Network Autoregressions—0
SA-NET.v2: Real-time vehicle detection from oblique UAV images with use of uncertainty estimation in deep meta-learning—0
Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data—0
EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous VariablesCode0
A Deep Learning Approach to Detect Lean Blowout in Combustion Systems—0
Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series—0
Concentration inequalities for correlated network-valued processes with applications to community estimation and changepoint analysis—0
Overlooked Poses Actually Make Sense: Distilling Privileged Knowledge for Human Motion Prediction—0
Multifractal cross-correlations of bitcoin and ether trading characteristics in the post-COVID-19 time—0
Interpretable Time Series Clustering Using Local Explanations—0
Predicting Future Mosquito Larval Habitats Using Time Series Climate Forecasting and Deep LearningCode0
Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel DataCode0
Time Series Forecasting Models Copy the Past: How to Mitigate—0
Conformal Prediction Bands for Two-Dimensional Functional Time Series—0
Signature-based models: theory and calibrationCode0
Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones—0
Forecasting euro area inflation using a huge panel of survey expectations—0
Sparse Bayesian State-Space and Time-Varying Parameter Models—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