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 1126–1150 of 6748 papers

TitleStatusHype
Progressive Cross-modal Knowledge Distillation for Human Action Recognition—0
Time is limited on the road to asymptopia—0
The Counterfactual-Shapley Value: Attributing Change in System Metrics—0
Expressivity of Hidden Markov Chains vs. Recurrent Neural Networks from a system theoretic viewpoint—0
Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market—0
ODformer: Spatial-Temporal Transformers for Long Sequence Origin-Destination Matrix Forecasting Against Cross Application Scenario—0
Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood RiskCode1
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
Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series ClassificationCode2
An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain InjuryCode0
Feature-Based Time-Series Analysis in R using the theft PackageCode1
Towards Coupling Full-disk and Active Region-based Flare Prediction for Operational Space Weather ForecastingCode0
Real-Time Massive MIMO Channel Prediction: A Combination of Deep Learning and NeuralProphet—0
Uncertainty Quantification for Traffic Forecasting: A Unified Approach—0
Approximate Extraction of Late-Time Returns via Morphological Component Analysis—0
HyperTime: Implicit Neural Representation for Time Series—0
Quantification of metabolic niche occupancy dynamics in a Baltic Sea bacterial community—0
Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series ForecastingCode2
Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy—0
TSInterpret: A unified framework for time series interpretabilityCode1
Estimating Sunlight Using GNSS Signal Strength from Smartphone—0
Representation learning of rare temporal conditions for travel time prediction—0
Liquid State Machine-Empowered Reflection Tracking in RIS-Aided THz Communications—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